Filtering search results using word clouds

ABSTRACT

Various methods and systems are provided for implementing a search engine that generates search results for a natural language description of music. A music description and categorization schema is defined to provide a common terminology and taxonomy. Music catalogs are ingested to generate a searchable catalog index. A natural language description of music is analyzed using various natural language processing techniques to generate corresponding musical features of the schema to be searched. A query string is generated from the musical features using a query term profile including one or more query term generators. A ranking score and other search result statistics are generated. Various visualizations can be provided, including a visualization of the translated search and a visualization of search results. In some embodiments, word clouds are provided to allow a user to filter search results by a selected matched feature. As such, users can efficiently review search results.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/017,322, filed Sep. 10, 2020, which is a continuation of U.S. patentapplication Ser. No. 15/881,416, filed on Jan. 26, 2018, now issued asU.S. Pat. No. 10,839,008, which claims priority to U.S. ProvisionalApplication No. 62/529,293, filed on Jul. 6, 2017. The entire contentsof each of the foregoing applications are incorporated by referenceherein in their entirety.

BACKGROUND

Film, television and advertising often rely on music to convey elementssuch as mood or theme. To identify the right piece of music for aparticular work, film directors, TV producers and other musicprofessionals provide sets of requirements that describe desired musicfor particular scenes. Procurement can occur within fairly constrainedtimelines and budgets. For example, a director for a TV episode mightask to find music for a particular scene within a day, or to find musicfor 15 different scenes within a few months. Music or media supervisorswill often use these music description requirements to draft a briefdescribing the kind of music the production team wants. Conventionally,in order to locate potential music candidates that fit this description,music or media supervisors might send the brief to people in theirnetwork to brainstorm possible matches. People may look up playlists tomanually search for potential matches or otherwise spark ideas. Often,the process involves browsing third party music catalogs to search forpotential matches.

SUMMARY

Embodiments described herein provide methods and systems for generatingsearch results from a natural language description of music. Generally,a music description and categorization schema can be defined to providea common language and taxonomy for searching. A dictionary can bedefined that translates domain-specific natural language (e.g., Englishwords and/or phrases describing characteristics of music) into musicalfeatures of the schema. By generating a searchable index using theschema and translating a natural language search input into the schema,a natural language search can be performed for desired music.

Generally, music catalogs can be ingested by translating song metadatainto the schema to create a searchable catalog index of songs. A naturallanguage description of music can be analyzed to generate correspondingmusical features of the schema using various natural language processingtechniques. For example, a raw input can be segmented into spans of text(e.g., sentences), and the spans can be assigned a valencecategorization describing the sentiment of a particular span using oneor more search patterns. References to music appearing in the raw input(e.g., a reference to a particular song or artist) can be identifiedfrom spans using one or more search patterns to identify search chunks,and the search chunks can be used to search for the reference in areference database. Spans for resolved references can be updated toreference spans. Musical features for references can be looked up in thereference database. Musical features for sentences can be generated byextracting chunks of text using one or more search patterns andtranslating the chunks into schema features using the dictionary. Assuch, the spans and corresponding valence categorizations can betranslated into musical features of the schema.

With the generated schema features to be searched and the searchableindex in the same schema language, a search can be performed. A querystring can be generated using the schema features to be searched askeywords by applying one or more query term generators from a query termprofile. Alternate (e.g., wildcard) queries can be generated usingrelaxed criteria defined in additional query term profiles. Boostmultipliers can be defined for query terms corresponding to schemacategories, musical features and/or valence levels to facilitategenerating ranking scores for search results. A baseline score (e.g., anall-match score) can be determined for a given query string, and thebaseline score can be used to initialize search result statistics.

Various visualizations can be provided to facilitate search resultreview. A visualization of the translated search can be provided thatincludes visualizations of translated schema features, including genresand related artists, that were applied in the search. The visualizationof the translated search can act as a mechanism for a user to understandand interpret the translation, and make changes, if necessary. Variousvisualizations of search results are possible, including visualizationsof primary and/or wildcard search result sets. In some embodiments, wordclouds are provided that allow a user to filter search results. Forexample, a word cloud can be generated with words and/or phrasescorresponding to matched schema features for a selected category (e.g.,a genre word cloud, a related artist word cloud, etc.) or group ofcategories (e.g., a word cloud for designated core schema features). Thesize of each feature in a word cloud corresponds to the number of searchresults that matched the feature. By selecting a particular feature in aword cloud, search results that matched that feature can be presented.In this manner, a user can efficiently review search results generatedfrom a natural language description of music, and select and sharecandidate songs.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used in isolation as an aid in determining the scope of the claimedsubject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of an exemplary computing environment, inaccordance with embodiments described herein;

FIG. 2 is a block diagram of an exemplary natural language searchsystem, in accordance with embodiments described herein;

FIG. 3 is a block diagram of an exemplary annotation component, inaccordance with embodiments described herein;

FIG. 4 is a block diagram of an exemplary translation component, inaccordance with embodiments described herein;

FIG. 5 is a block diagram of an exemplary matchmaking component, inaccordance with embodiments described herein;

FIG. 6 is a block diagram of an exemplary user application, inaccordance with embodiments described herein;

FIG. 7 is an exemplary user interface depicting a natural language inputcomprising a description of music, in accordance with embodimentsdescribed herein;

FIG. 8 is an exemplary user interface depicting a visualization of atranslated search, in accordance with embodiments described herein;

FIG. 9 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 10 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 11 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 12 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 13 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 14 is an exemplary user interface depicting a visualization ofsearch results, in accordance with embodiments described herein;

FIG. 15 is a flow diagram showing an exemplary method for ingestingmusic, in accordance with embodiments described herein;

FIG. 16 is a flow diagram showing an exemplary method for generating anannotated input, in accordance with embodiments described herein;

FIG. 17 is a flow diagram showing an exemplary method for generatingschema features from an annotated input, in accordance with embodimentsdescribed herein;

FIG. 18 is a flow diagram showing an exemplary method for executing asearch based on a set of schema features, in accordance with embodimentsdescribed herein;

FIG. 19 is a flow diagram showing an exemplary method for ranking searchresults, in accordance with embodiments described herein;

FIG. 20 is a block diagram of an exemplary computing environmentsuitable for use in implementing embodiments described herein.

DETAILED DESCRIPTION Overview

As described above, music or media supervisors often use musicrequirements to draft a brief (which may also be referred to as anoffer) describing what kind of music the production team wants. Variousexample briefs may be found in Provisional App. No. 62/529,293 at pp.6-8. For example, a brief might read:

-   -   “Hey guys, I'm helping a TV commercial client find music for a        spot about a night drive with the whole family in spring time.        The music should be warm, positive and maybe a little bit        melancholic, but with a nice pace. We are open to Singer        Songwriter a la Bon Iver up to Indie Folk a la The Lumineers and        Of Monsters and Men. We are also open to artists like Bear's        Den, and maybe that guy The Tallest Man on Earth. We do not want        new americana, and nothing too mainstream. A song with a mood we        love is In Every Direction by Junip. A modern take on the genre        would be a plus and lyrics about hope in relationships. I've got        a budget for this of 15K. Thanks!”

To identify songs that fit the description in this example, the music ormedia supervisors may be interested in reviewing multiple potential songcandidates. Conventionally, in order to locate music that potentiallyfits this description, music or media supervisors might send the briefto people in their network to brainstorm ideas. People may look upplaylists to search for potential matches or spark ideas. Often, theprocess involves browsing third party music catalogs to search forpotential matches. For example, music or media supervisors may searchcatalogs from labels, publishers, music licensing agencies, etc. andbrowse results for potential matches. Such catalogs often utilize uniqueinterfaces and search syntaxes, so the search process involves manuallyadapting a rich textual description into a variety of search contexts.

Conventional methods for identifying music that matches a description ofmusical features (e.g., from a brief) have several shortcomings becauseof the time and resource-intensive processes involved. For example, toidentify songs within a single catalog that match a description ofmusical features, the description needs to be translated into thecatalog's particular search syntax and entered into the catalog's searchinterface. Currently, this requires human effort, which can be timeconsuming, inefficient and susceptible to error. This process may berepeated for multiple catalogs, compounding the inefficiencies. Inaddition, having identified candidate songs using various catalogs,brainstorming and word of mouth, conventional methods do not provide away to objectively rank, review, visualize or manage search results fromdisparate sources. As such, processes that streamline the search andreview processes are integral to the efficient identification of musicfrom a description of musical features.

Embodiments described herein provide simple and efficient methods andsystems for generating search results from a natural language searchrequest for music (like the brief above). At a high level, a musicdescription and categorization schema can be defined to provide a commonterminology (schema language) and taxonomy (schema categories) for asearch architecture. Various music catalogs can be ingested into acatalog index by translating song metadata into musical features of theschema. As such, a natural language description of musical features(e.g., a brief) can be analyzed to generate corresponding musicalfeatures of the schema to be searched, and the catalog index can besearched for potential matches. More specifically, a search query can begenerated from generated musical features and a corresponding searchexecuted on the catalog index, or portions thereof (e.g., one or moreingested catalogs). Various ranking techniques can be employed, and aninterface can be provided for visualizing and reviewing search results.

By way of background, natural language search processing can be improvedusing contextual knowledge of a particular domain, such as music. Manydomains are well-suited to analysis and categorization in multiple wayssuch as, in the case of music, by energy, mood, rhythm, tempo, genre,etc. Furthermore, a natural language description in the context of agiven domain can evoke a normalized terminology and meaning. The word“dreamy”, for example, may have multiple meanings in a general sense,but in the context of a music search, it implies specificcharacteristics, e.g., a mellow energy, an ethereal rhythm, a reflectivemood. In a general sense, contextual observations can be made within anydomain. With an appropriate context, the language of a search requestand a description of a potential search candidate can converge to anextent that suggests a match.

Accordingly, a search architecture can be designed around a descriptionand categorization schema (whether pre-defined or adaptable). Thedescription and categorization schema can be designed based on aselected domain (e.g., music, audio books, podcasts, videos, events,ticketing, real estate, etc.). For example, a music description andcategorization schema can be used as a basis for articulating a requestfor music and/or for describing features of an individual piece ofmusic. Generally, musical features can be grouped and/or faceted intocategories (such as vibe, tempo, genre), sub-categories (e.g., rhythm,energy, and mood might be sub-categories of vibe) and instances of agiven category or subcategory (e.g., Americana might be a musicalfeature identified as an instance in a genre category). As described inmore detail below, category instances may be utilized as keywords. Asdescribed in more detail below, the particular categories,sub-categories and/or category instances that describe domain-specificcontent (e.g., a piece of music) can have implications for querygeneration and search result weighting directives.

Table 1 illustrates an exemplary music description and categorizationschema. In the schema depicted in Table 1, categories can have instancesthat are assigned from the schema (e.g., tempo=downtempo) or free form(e.g., artist=xx). Likewise, categories and sub-categories can haveinstances that are partitioned (e.g., release era for a given songcannot have multiple selections) or blended (e.g., genre may havemultiple selections for a given song). Some categories may have categoryinstances that include the text of another category instance for thatcategory (e.g., genre may include category instances for both pop anddance pop) or whose parts may have independent meaning. Accordingly,category instances longer than one word (e.g., genre=indie rock) can bedelimited in various ways to support search functionality. For example,the use of a particular delimiter (e.g., a space) can facilitatetokenizing the category instance to generate multiple keywords ofvarious units (e.g., “indie rock,” “indie” and “rock”) and/or matching aparent or related genre (pop) with a sub-genre (dance pop). In thismanner, the use of a particular delimiter can provide structure thatfacilitates partial matching.

TABLE 1 Category Sub-category Instances Characteristics Vibe Rhythmstill Assigned ethereal Blended rhythmic danceable Energy minimalAssigned mellow Blended dynamic intense Mood emotional Assignedemotional_reflective Blended reflective happy euphoric quirky ProductionExplicitness clean Assigned explicit Partitioned Layersvocals_with_music Assigned instrumental Partitioned male_lead_voxfemale_lead_vox producer_main_artist Recording studio Assigned livePartitioned Arrangement General electric_or_electronic Assignedorganic_electric_or_electronic Blended organic_acoustic_or_electricacoustic Section vocal_intro Assigned Instrumentation vocal_outtroBlended instrumental_intro instrumental_outtro piano_intro piano_outtroacoustic_guitar_intro acoustic_guitar_outtro electric_guitar_introelectric_guitar_outtro strings_intro strings_outtrodrums_or_percussion_intro drums_or_percussion_outtro Shape N/Acalm_intro_or_fade_in Assigned normal_intro Blended energetic_introcalm_outtro_or_fade_out normal_outtro energetic_outtro flat_throughoutascending_throughout descending_throughout multiple_crescendos Tempo N/Adowntempo Assigned midtempo Partitioned uptempo Popularity N/A obscureAssigned emerging Blended noteworthy known popular mainstream megastarRelease Abstract vintage Assigned retro Partitioned generation_xcontemporary new unreleased Era 20s Assigned 30s Partitioned 40s 50s 60s70s 80s 90s 00s 2010_and_later Instruments N/A drums Assignedother_percussion Blended drum_machine piano organ acoustic_guitarelectric_guitar strings brass synths Genre N/A Instances may be definedbased on Assigned typically used genres and sub-genres Blended RelatedArtists N/A Unbounded number of artist names Freeform Blended Themes N/ALyrical themes (e.g., from search Freeform requests) or lyrical indexes(e.g., Blended from content) Translated N/A Standardized lyrical themese.g. love, Assigned Themes good times, travel, loneliness Blended ArtistNames N/A Unbounded artist name references Freeform (e.g., from searchrequests) Blended Song Names N/A Unbounded song name references Freeform(e.g., from search requests) Blended Modifiers N/Ainstrumental_stem_available Assigned vocal_stem_available Blendedfull_stems_available

A description and categorization schema can be implemented in variousways. By way of a nonlimiting example, a dictionary can be defined thattranslates natural language words and/or phrases to features (e.g.,musical features) of the schema, and the dictionary can be stored in adatabase. Domain-specific dictionaries can be used to extract featuresfrom an input description. For example, a music dictionary can be usedto extract musical features from a description of music (e.g., by“translating” Ngrams from the description to the schema language). Insome embodiments, a reference database can be generated that relates aparticular search reference (e.g., in the case of music, artists and/orsongs) with corresponding features of the schema. Accordingly, in thecase of music, references to specific songs or artists appearing in adescription of music (e.g., “a song with a mood we love is Born in theUSA”) can be identified using the reference database, and musicalfeatures corresponding to that reference can be retrieved. Additionallyand/or alternatively, an index of potential search targets (e.g., asearchable music catalog index) can be generated using the schema. Inthis manner, the index can be searched for extracted features of theschema to generate potential search results.

Various ranking techniques can be employed to rank search results for aquery. For example, boost multipliers can be defined for schemacategories and/or valence levels; query terms can be generated usingquery term generators for corresponding schema categories, schemafeatures and/or valence levels; and ranking scores can be generated forsearch results based on boost multipliers for corresponding matchedquery terms. Additionally and/or alternatively, counts can be maintainedof returned search results within schema facets (e.g., categories,sub-categories, category instances/keywords). In some embodiments, asystem document can be generated that includes a representation of allschema features appearing in a catalog (e.g., all musical featuresappearing in the music catalog index), and a search can be executed onthis “all-match document” using a modified representative version of thequery (e.g. excludes negative terms, uses representative categorykeywords, etc.). The resulting “all-match score” can be used as abaseline maximum score used to initialize search result statistics. Assuch, ranking scores of search results can be converted to normalizedscores (e.g., on a fixed scale such as five stars).

Various visualizations can be employed to facilitate reviewing searchresults. For example, a user performing a search may be presented with avisualization of musical features that were identified from a naturallanguage input description of music. Various results visualizations canbe presented, for example, including visualizations of resultsdetermined to be the most relevant (e.g., using ranking scores of searchresults), word clouds reflecting musical features that matched the mostsearch results within one or more schema categories (e.g., designatedcore features, genre, related artists, etc.), and/or visualizations ofwildcard search results that matched a relaxed query. As a user reviewssearch results using these visualizations, a list of selected candidatesongs can be stored, displayed and shared with others.

As such, search results can be generated from a natural languagedescription based on a description and categorization schema. Searchresults can be ranked using boost multipliers for corresponding matchedquery terms from the query string, and visualizations can be presentedto facilitate review and selection of desired search results.

Exemplary Search Environment

Referring now to FIG. 1, a block diagram of exemplary environment 100suitable for use in implementing embodiments of the present disclosureis shown. Generally, environment 100 is suitable for search engines andnatural language processing, and, among other things, facilitates thegeneration of search results from a natural language description (e.g.,of music). Environment 100 includes user devices 110 a and 110 n,catalogs 120 a and 120 n (e.g., third party music catalogs), naturallanguage search system 130 and network 140. Generally, user devices 110a and 110 n can be any kind of computing device capable of facilitatingthe generation of search results from a natural language description(e.g., of music). For example, in one embodiment, user device 110 can bea computing device such as computing device 2000, as described belowwith reference to FIG. 20. For example, user device 110 can be apersonal computer (PC), a laptop computer, a workstation, a mobilecomputing device, a PDA, a cell phone, or the like. Catalogs 120 a and120 n may contain electronic collections of music accessible to naturallanguage search system 130. For example, electronic collections of musicmay reside on third party computing systems, which may be accessible,for example, via APIs 125 a and 125 n. The components of environment 100may communicate with each other via a network 140, which may include,without limitation, one or more local area networks (LANs) and/or widearea networks (WANs). Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets, and the Internet.

In embodiments relating to the music domain, a user can search for musicby inputting a natural language description of music into an application(e.g., app 115) on a user device (e.g., user device 110 a). Theapplication may be a stand-alone application, a mobile application, aweb application, or the like. Additionally and/or alternatively, theapplication or a portion thereof can be integrated into the operatingsystem (e.g., as a service) or installed on a server (e.g., a remoteserver).

In the embodiment illustrated in FIG. 1, app 115 can receive a naturallanguage input comprising a description (e.g., an offer or search ofdesired music) and transmit the description to natural language searchsystem 130 for processing. Generally, natural language search system 130analyzes the description to identify features (e.g., musical features),constructs and executes a search based on the identified features, andreturns search results to app 115. To support searching, in someembodiments, natural language search system 130 can ingest catalogs ofsearch target information (e.g., catalogs 120 a and 120 n) to constructa searchable catalog index (e.g., of songs), as described in more detailbelow.

Turning now to FIG. 2, FIG. 2 illustrates exemplary natural languagesearch system 200, which may correspond to natural language searchsystem 130 in FIG. 1. In the embodiment depicted in FIG. 2, naturallanguage search system 200 includes dictionary 210, catalog index 220,reference database 230, query term profiles 240 a through 240 n,ingestion component 250, reference search component 260, annotationcomponent 270, translation component 280 and matchmaking component 290.Natural language search system 200 may be implemented using one or morecomputing devices (e.g., a web server, server with one or more externaldatabases, etc.). In embodiments where the system is implemented acrossmultiple devices, the components of natural language search system 200may communicate with each other via a network, which may include,without limitation, one or more local area networks (LANs) and/or widearea networks (WANs).

Natural language search system 200 decomposes the natural languagesearch problem into feature translation and matchmaking. Featuretranslation is language translation from a domain-specific humanlanguage (e.g., English language search requests for music, audio books,podcasts, videos, events, ticketing, real estate, etc.) to a descriptionand categorization schema. In embodiments relating to music searches,natural language search system 200 implements a music description andcategorization schema. For example, a dictionary can be generated totranslate domain-specific words and/or phrases (e.g., describing musicalcharacteristics) to musical features in the schema, and a correspondingdictionary database file stored as dictionary 210. In some embodimentsrelating to music searches, dictionary 210 translates synonyms to acommon musical feature, to normalize descriptions with similar meanings.An example dictionary database file for a music description andcategorization schema may be found in Provisional App. No. 62/529,293 atpp. 144-168. As will be appreciated, dictionary 210 can be used forvarious translation purposes. For example and as explained in moredetail below with respect to ingestion component 250, when ingestingthird party catalogs into a music catalog index, dictionary 210 can beused to perform language translation of unstructured metadata (e.g.,song descriptions) to musical features of the schema. Additionallyand/or alternatively, dictionary 210 can be used to translate a naturallanguage description of music to musical features of the schema, tofacilitate generating a query string. Various other uses for dictionary210 will be understood by those of ordinary skill in the art.

A catalog index (e.g., catalog index 220) can be generated to indexsearch targets using one or more data structures that implement thedescription and categorization schema. By way of nonlimiting example, inembodiments relating to music a catalog document can be generated foreach song, with the index comprising a collection of catalog documentsfor each song in the index. In some embodiments, songs from third partycatalogs can be ingested (e.g., using ingestion component 250) viacatalog plugin modules tailored to the source catalog to generatecatalog documents for the index. A catalog document can include variousfields for corresponding musical features of the schema. For example,fields may be designated for musical features such as identificationinformation (e.g., song ID, third party catalog ID, artist, album,etc.); musical features corresponding to available song metadata (e.g.,genre, instruments, related artists, lyrics); and musical featurestranslated to the music description and categorization schema (e.g.,vibe, production, arrangement, shape, tempo, popularity, release,instruments, etc.). Various other indexing data structures can beimplemented by those of ordinary skill in the art and are contemplatedwithin the present disclosure.

Table 2 illustrates an exemplary definition of catalog document fields.Some fields may be designated as required for a particularimplementation (e.g., id, songName, artistName, releaseDate, etc.),while other fields may be designated as optional and need not bepopulated (e.g., albumName). Additionally and/or alternatively, one ormore fields may be indexed, and one or more indexed fields may begenerated to facilitate searching. For example, one field may bedesignated for a songName, while a second tokenized field (e.g.,searchableSongName) may be generated to optimize searching, for example,by redacting terms that are unlikely to denote meaningful searchcontent, such as music production specific terms (e.g., remastered,remix, version, featuring, deluxe edition, expanded edition, anniversaryedition, LP, EP, bonus track(s), etc.); song name punctuation marks(e.g., period, ampersand, brackets, etc.) and/or digits and ordinalnumbers (since they may denote production or release date references asopposed to meaningful search content). In another example, an indexedfield (e.g., a field which may include freeform text) may applytokenizer rules such as case change, alpha to numeric conversion,removing English and/or domain-specific stopwords (e.g. “and”, “the”,“on”, “all-in”, etc.), applying stemming (e.g., Porter stemming forEnglish), synonym generation and the like. Various other tokenizer ruleswill be apparent to those of ordinary skill in the art and arecontemplated within the present disclosure.

TABLE 2 Field Description id Unique identifier for the song (e.g., UUID,URI, combinations thereof, etc.) catalogId Unique identifier for thethird party catalog containing the song songName Song namesearchableSongName Indexed field including song name tokens isrcInternational Standard Recording Code songPreviewUrl URL to access thesong for preview and playback when rendering search results. Forexample, the songPreviewUrl can be used as an HTML embed in a searchresult web UI or a launch target for a browser pane or tab songArtUrlURL to access artwork for the song (e.g., single or album art)songDuration Integer length of the song in milliseconds artistNameArtist name artistUrl URL to artist or other site describing theartist's works albumName Name of the album, EP, single package, etc.,containing the song searchableAlbumName Indexed field including albumname tokens albumUrl URL to album or other site describing the workreleaseDate Release date of the song cSF Core schema features - categoryinstances from “core” designated schema categories (e.g., vibe,production, arrangement, shape, tempo, popularity, release, andinstruments) gF Genre Features - category instances from genre categorygF_Facet Genre Features - category instances from genre category thatare not tokenized by whitespace (e.g., “dance pop” is only added to thegF field index as “dance pop”) iF Instruments Features - categoryinstances from Instruments category raF Related Artists Features -category instances from Related Artists category aF Freeform textdescription of the content being indexed, for example, an untranslatedsong description from a third party catalog. tF Themes Features -freeform text field for song lyrics, song name tokens and/or albumn nametokens.

Fields may be designated to store applicable features (e.g., categoryinstances from designated schema categories) for a given search target.Fields may be designated for features from one schema category (e.g., agenre features field) and/or multiple schema categories (e.g., a fieldmay contain category instances from designated “core” schema categoriessuch as vibe, production, arrangement, shape, tempo, popularity, releaseand/or instruments). For example, the gF field in Table 2 can bepopulated with genre instances for a given song. As another example, thecSF field in Table 2 can be populated with category instances for thevibe, production, arrangement, shape, tempo, popularity, release andinstruments categories.

Fields may be tokenized (e.g., multiple category instances within agiven field may be comma delimited) to facilitate matching individualcategory instances present in a corresponding catalog document. Forexample, a genre field may use commas to separate tokens of genreinstances and may use whitespace to separate multiple parts ofmulti-part instances (e.g., “dance pop, etherpop, indie poptimism,pop”), and tokens and query terms may be generated in lower case form tofacilitate matching. In this manner, queries on such a field can matchtokens of present category instances, and queries for parent genres canalso match sub-genres. In some embodiments, related genre matching canbe disabled (e.g., so a search for “classical” does not match “classicalcrossover”) by searching using a different delimiter, designating anadditional non-tokenized field (e.g., gF_Facet in Table 2), orotherwise. Generally, any schema category can be used to generate acorresponding tokenized field to facilitate searching for tokens withinthat category (e.g., a tokenized field for Themes Features canfacilitate searching for tokenized themes from song lyrics, song names,song albums, etc.). Tokenization can also facilitate performingstatistical calculations for matched tokens to drive rankings andvisualizations of search results (e.g., word clouds that can filtersearch results by schema category and/or matched token), as described ingreater detail below.

One particular issue that can arise in the context of music occurs whena song is re-released without updating song content (or with only aremastering applied). In this scenario, the new “version” often has anupdated release date. This can create a problem in a search regime thatutilizes release date as a searchable field. This issue can be resolved,for example, by utilizing “release vibe” and/or “era” categories, andpopulating a field in a catalog document for the re-release withcorresponding features for the original release. In the exemplarycatalog document definition illustrated in Table 2, this is accomplishedby assigning original release features in the cSF field.

Using the catalog document fields illustrated in Table 2, an examplecatalog document in JavaScript Object Notation format may look asfollows. This example document is merely meant as an example. Othervariations of documents, data structures and/or corresponding fields maybe implemented within the present disclosure.

{  ″id″:″16e6ced0-eff1-4349-bda2-f427aa7597e0::spotify:track:2IO7yf562c1zLzpanal1DT″, ″catalogId″:″16e6ced0-eff1-4349-bda2-f427aa7597e0″, ″songName″:″Gasoline″,  ″searchableSongName″:″Gasoline″, ″isrc″:″USQY51704397″,″songPreviewUrl″:″http://open.spotify.com/embed?uri=spotify:track:2IO7yf562c1zLzpanal1DT″,  ″songDuration″:199593,  ″artistName″:″Halsey″,″artistUrl″:″http://open.spotify.com/embed?uri=spotify:artist:26VFTg2z8YR0cCuwLzESi2″,  ″albumName″:″BADLANDS (Deluxe)″, ″searchableAlbumName″:″BADLANDS″, ″releaseDate″:″2015-08-28T07:00:00Z″,  ″cSF″:″danceable, dynamic,emotional_reflective, explicit, vocals_with_music, studio,organic_electric_or_electronic, energetic_intro,calm_outtro_or_fade_out, multiple_crescendos, midtempo, popular,mainstream, new, 2010_and_later″,  ″gF″:″dance pop, etherpop, indiepoptimism, pop, post-teen pop, tropical house″,  ″aF″:″Halsey is thealias of New York-based pop artist Ashley Frangipane. The New Jerseynative took her moniker from a New York L train subway stop, and heradopted city plays a large role in both the sound and lyrics of herdark, gritty electro pop, which has been compared to acts like Chvrchesand Lorde.″,  ″raF″:″Halsey, Zella Day, Melanie Martinez, That Poppy,Broods, Ryn Weaver, Lorde, Betty Who, Oh Wonder, Troye Sivan,MisterWives, MØ, Bea Miller, Hayley Kiyoko, BØRNS, Marina and theDiamonds, The Neighbourhood, Tove Lo, Alessia Cara, Marian Hill,Bleachers” }

In this manner, a catalog index can be generated that includes one ormore records describing cataloged search targets and correspondingfeatures of the description and categorization schema. As such, thecatalog index (or a portion thereof) can be the target for searchqueries. As described in more detail below, in some embodiments, one ormore system files may be generated that include all features appearingin the catalog index. For example, an all-match document (e.g.,all-match document 225) may be generated that includes a representationof all category instances (e.g., schema features) appearing in theindex, and the all-match document may be used as a baseline to assistwith ranking search results.

Generally, descriptions of music (like the briefs described above) ofteninclude references to specific songs or artists (e.g., “A song with amood we love is In Every Direction by Junip.”) and may includehyperlinks to the song (e.g., links to YOUTUBE® or SPOTIFY® webpages fora song). Accordingly, in some embodiments, references can be identifiedfrom a natural language description of music using natural languageprocessing, as described in more detail below. The identificationprocess can be facilitated by a reference database (e.g., referencedatabase 230). For example, songs from third party catalogs can beingested (e.g., using ingestion component 250), and reference database230 can be populated with records of artists/songs and correspondingmusical features of the music description and categorization schema. Incertain embodiments, reference search component 260 searches referencedatabase 230 for references using search chunks identified from adescription of music. If reference search component 260 locates a match,reference search component 260 can retrieve an identifier for thematched artist/song and/or corresponding musical features. For example,corresponding musical features may be accessed from the referencedatabase or from another component such as catalog index 220. As such,reference database 230 can be used to identify musical features forreferences identified from a natural language description of music.These concepts will be explained in more detail below.

With continued reference to FIG. 2, ingestion component 250 can be usedto ingest catalogs (e.g., catalogs 120 a and 120 n in FIG. 1) andpopulate one or more searchable indexes and/or databases such as catalogindex 220 and/or reference database 230. In the context of music, thisprocess involves accessing song metadata from music catalogs andprocessing it to populate corresponding fields of the one or moresearchable indexes and/or databases. As described above, such fields caninclude identification information (e.g., song ID, third party catalogID, artist, album, etc.); available song metadata (e.g., genre,instruments, related artists, lyrics); and translated musical features(e.g., vibe, production, arrangement, shape, tempo, popularity, release,instruments, etc.).

Generally, music content and song metadata are primarily available viaagency, label, distributor, publisher and artist music catalogs.However, there is no standardized format for song metadata among thevarious music catalogs. Rather, song metadata is a complex web ofstructured (e.g., machine generated) and unstructured (e.g., humangenerated) data that can be formatted in various ways and is accessiblethrough many different infrastructures and API's. Accordingly, metadatatranslation can be accomplished using format conversion of structuredmetadata and/or language translation of unstructured metadata intomusical features of the music description and categorization schema.Since catalogs may be organized in different ways, the particularmetadata translation technique can depend on the source catalog beingingested. In this manner, ingestion component 250 includes tailoredtechniques (e.g., implemented via catalog plugin modules) for metadatatranslation.

With respect to translation of metadata by format conversion,relationships can be defined to convert structured metadata from acatalog to features of the schema. By way of nonlimiting example, acustom JavaScript Object Notation (JSON) importer can access formattedsong metadata from a catalog owner's database and convert the metadatato the schema format. In another example, an importer for the SPOTIFY®Virtual Catalog can access SPOTIFY® song identifiers, retrieve songmetadata, and convert the metadata to a format compatible with theschema. An example format conversion for translating SPOTIFY® songmetadata to a music description and categorization schema may be foundin Provisional App. No. 62/529,293 in Appendix A.

With respect to translation of metadata by language translation, somecatalogs may include one or more metadata fields with a natural languagedescription of music. In these instances, this unstructured metadata(e.g., the natural language description of music) can be translated tomusical features of the schema (e.g., using ingestion component 250and/or translation component 280). In some embodiments, the translationalgorithm used to translate unstructured metadata (e.g., convertunstructured metadata to musical features of the schema) can be the samealgorithm used to translate search requests (e.g., identify musicalfeatures from a natural language description of music), as described inmore detail below. For example, in the embodiment illustrated by FIG. 2,translation component 280 may perform both translations.

In either event, whether by format conversion of structured metadata orlanguage translation of unstructured metadata, musical features of theschema can be generated for songs/artists in music catalogs.Accordingly, ingestion component 250 can use identification information,available song metadata and/or translated musical features of the schemato populate one or more searchable indexes and/or databases, forexample, like the catalog index (e.g., by generating entries such ascatalog documents for songs) and the reference database (e.g., withfields for structured song metadata, most popular song metadata, etc.).

With continued reference to FIG. 2, natural language search system 200decomposes the natural language search problem into feature translationand matchmaking. Generally, natural language search system 200translates a description (e.g., a search request, unstructured metadata,etc.) using natural language processing to generate correspondingfeatures in the schema (e.g., using annotation component 270 discussedbelow and translation component 280). In this sense, natural languagesearch system 200 first converts an input description to a commontextual form (the schema). It then finds and ranks related content viatext search algorithms (e.g., using matchmaking component 290).

With respect to feature translation, FIG. 3 illustrates exemplaryannotation component 300 (which may correspond to annotation component270 of FIG. 2), and FIG. 4 illustrates exemplary translation component400 (which may correspond to translation component 280 of FIG. 2). Inthese embodiments, feature translation of a natural language description(e.g., natural language searches, unstructured metadata, etc.) isaccomplished in two steps. First, a natural language description (theraw description) is annotated with information identified from thedescription (e.g., terms metadata, spans of the description andcorresponding sentiments, references, hyperlinks, etc.). For example, araw input can be segmented into units of words/characters (spans)identified by character locations in the description. In this sense, anannotated input can be generated comprising one or more documents and/ordata structures that include start & stop character numbers for a givenspan, identification of spans that include a reference or a hyperlink,and a characterization of the sentiment of a particular span (e.g., avalence categorization). Then, the annotated input can be used toidentify corresponding features in the schema.

Annotation component 300 includes valence generator 310, referenceidentification component 320, annotation generator 330 and sentenceidentification component 340. Generally, sentence identificationcomponent 340 segments a natural language input to identify spans, aswould be understood by a person of ordinary skill in the art. Valencegenerator 310 assigns each span a valence categorization selected from aset of valence levels, reference identification component 320 identifiesreferences (e.g., song, artist, hyperlink, etc.), and annotationgenerator 330 generates an annotated input comprising spans andvalences.

Generally, a natural language description (such as, in the music domain,the briefs described above) can include structured inputs. Accordingly,in some embodiments, annotation component 300 can identify structuredinputs as metadata spans by performing sentence detection on a raw input(e.g., using sentence identification component 340), recording resultingspans, and searching for a defined format (e.g., <header>: <data>) ofdefined fields (e.g., Request Title, Description, Budget, End Date,Deadline, Media, Territory, Term, etc.). Resulting metadata spans can bestored separately from spans that include song content (e.g., artist,song title, etc.), so the content spans can be analyzed separately.

In the embodiment illustrated in FIG. 3, valence generator 310 accessesa segmented natural language input and generates valence categorizationsfor spans (e.g., sentences) to characterize the sentiment of the spans.As used herein, valence generally refers to a characterization and/or aclassification of a particular sentiment e.g. positive, neutral, ornegative. Various techniques for generating a valence categorization canbe implemented. By way of nonlimiting example, a set of valence levelscan be defined on a scale that quantifies the degree ofpositivity/negativity for a particular span (e.g., a sentence). Forexample, a scale can be defined having any number of levels (e.g., 5,10, 50, etc.) with one end of the scale corresponding to a positivesentiment and the opposite end corresponding to a negative sentiment.Table 3 depicts an exemplary set of valence levels that can beimplemented in some embodiments.

TABLE 3 Enum Description HighlyPositive Used to indicate a highlypositive span Positive Used to indicate a positive span Neutral Used toindicate a neutral span Negative Used to indicate a negative spanHighlyNegative Used to indicate a highly negative span

By way of nonlimiting example, a positive category (e.g., Positive) canbe used to indicate a reference (e.g., song, artist, hyperlink, etc.)where the intended musical features are implied by the target content,and a higher degree positive category (e.g., HighlyPositive) can be usedto indicate express preferences (e.g., musical features/keywords) wherethe intended musical features are stated in the natural language. Forexample, a span (e.g., a sentence) can be assumed to be HighlyPositiveunless a negative search pattern hits, as described in more detailbelow. In this manner, a low degree negative category (e.g., Negative orHighlyNegative) can be used to indicate spans that matched one or moredefined negative search patterns. In some embodiments, other valencecategorization methodologies may be implemented, for example, usingdifferent valence levels and/or categories, different assumptions and/ordifferent search patterns. Other variations of valence categorizationmethodologies are contemplated and can be implemented within the presentdisclosure.

Continuing with the example above, valence generator 310 can assume eachspan is positive (e.g., HighlyPositive), execute one or more negativesearch patterns, and change a valence categorization (e.g., to Negativeor HighlyNegative) if a negative search pattern hits. For example,search patterns can be defined (e.g., using a regular expression, i.e.,regex) to search a natural language input (e.g., a sentence from the rawinput) for words and/or phrases that can indicate the beginning and/ortermination of a negative sentiment. By way of nonlimiting example, anegativeValence Regular Expression Pattern can be defined, such as:

(?i)(nothing|not be|not a|noone like|nobody like| than | less | minus|do not want|don't want|don't have|won't need|will not need|can'tbe|cannot be|can't have|cant have|cannot have|shouldn't|without|aren'tinto|is not|are not|are not into|too|not looking for|not really lookingfor|not after|avoid|stay away from|nor should|but not(?!.*limitedto)|yet not|yet is not|not heard|\\bno\\b|\\bnot\\b(?!.*against))(.*)Likewise, a findEarliestNegativeTerminatorIndex Terminators pattern canbe defined, such as:

{ “would rather have”, “but preferably”, “but rather”, “but it”, “ but”, “.but ”, “ because ”, “ since ”, “ just ”, “and can ”, “ so anything”, “ however ”, “ can be ”, “ still need ”, “ more ”, “ are going to ”,“ in a similar ”, “, artist like”, “, band like”, “, singer like”, “\n”,“. ”, “)”, “]”, “...”, “...” }Accordingly, valence generator 310 can search a given span using one ormore search patterns, and change a valence categorization if one or morepatterns hit the span (e.g., the span contains words/phrases containedin both beginning and termination search patterns). In this manner,valence generator 310 can assign a valence categorization for spans ofan input (e.g., for each sentence of a natural language description).

Reference identification component 320 generally identifies references(e.g., to a song, artist, hyperlink, etc.) from a natural language input(e.g., a span). For example, hyperlinks can be identified andcorresponding references identified using the hyperlink. Additionallyand/or alternatively, express references contained in natural languagecan be identified. In various embodiments, the identification process isfacilitated by one or more indexes and/or databases (e.g., referencedatabase 230). For example, a reference database can be searched formatches, and a resulting match (e.g., a song) can be returned withidentification information and/or features for the match (e.g., fromreference database).

In some embodiments, reference identification component 320 can identifyreferences from hyperlinks appearing in a natural language input andcorresponding features of the schema. For example, referenceidentification component 320 can run a defined URL search pattern toidentify hyperlinks, determine whether a corresponding URL platform(e.g., YOUTUBE®) is supported, resolve an identified hyperlink to searchterms and search a database of references using resolved search terms.If a reference is located, reference identification component 320creates a reference span and stores the identified hyperlink.

Generally, one or more search patterns can be defined (e.g., usingregex) to search a natural language input (e.g., a sentence or otherspan from the raw input, the full input, etc.) for words and/or phrasesthat can indicate a hyperlink. For example, a matchUrlInContent RegularExpression Pattern can be defined, such as:

(http|ftp|https)://([\\w_−]+(?:(?:\\.[\\w_−]+)+))([\\w.,@?{circumflexover ( )}=%&:/~+#− ]*[\\w@?{circumflex over ( )}=%&/~+#−])?Accordingly, reference identification component 320 can identify ahyperlink from a given span by searching the span using one or moresearch patterns. A hit can indicate, for example, that the span is ahyperlink.

In some embodiments, this hyperlink can be used to look up informationabout the hyperlink target that can be used to search a referencedatabase (e.g., title, artist name, etc.). For example, some platformssuch as YOUTUBE® provide an API that allows third parties to accessinformation about content hosted on the YOUTUBE® platform. As such,reference identification component 320 can determine whether anidentified hyperlink points to a supported platform (e.g., by using asearch pattern to identify supported platforms), and if so, resolve thehyperlink to search terms (e.g., using the platform's API). For example,for YOUTUBE® hyperlinks, reference identification component 320 can usethe YOUTUBE® API to retrieve the name of the target video (usuallycontaining some form of the artist and song name) and generatecorresponding search terms (e.g., the entire video name, a segment ofthe video name, with or without redactions and/or modifications, etc.),as would be understood by a person of ordinary skill in the art.Additionally and/or alternatively, a target webpage can be analyzed forrelevant search terms. As such, reference identification component 320can resolve an identified hyperlink to one or more search terms forsearching a reference database.

Generally, a reference database (e.g., reference database 230) can besearched for an identified search term (e.g., a search chunk identifiedfrom a span from a natural language input, a retrieved video title,etc.). For example, reference identification component 320 can callreference search component 260 to search reference database 230. Variousnatural language processing techniques can be implemented to facilitatesearching. By way of nonlimiting example, redactions can be applied toincompatible characters and production terms (e.g., instrumentalversion, version xxx, etc.). In a preferred embodiment, a resultingredacted search term can be stored for comparison with multiple results.Additionally and/or alternatively, redactions and/or modifications canbe applied to defined strings/terms (e.g., removing “'s”; removing “by”;changing “theme song” to “theme”; etc.) to improve the likelihood that asearch hit occurs. In this manner, reference search component 260 canrefine search terms and search reference database 230 for the refinedsearch terms. For example, reference search component 260 may search asong field of reference database 230. If there are no results, text ofthe search term appearing after selected indicators such as “-” and/or“(” can be removed and the search rerun. If there are no results, a newsearch chunk can be allocated with an updated position based on an openquote, if available (e.g., in case the text that follows an open quoteis the beginning of a song search term) and the search rerun. If any ofthese searches results in multiple matches, the matches can be comparedwith the search term stored before applying some or all redactionsand/or modifications to identify the best match. If a reference islocated, reference identification component 320 can create acorresponding reference span (e.g., including a corresponding valencecategorization such as Positive) and store the identified hyperlink.Otherwise, a reference can be identified as unresolved. Other variationsof search methodologies can be implemented within the scope of thepresent disclosure.

Additionally and/or alternatively, reference identification component320 can identify references (e.g., song or artist) appearing in the textof a natural language input (e.g., a span). For example, one or moredefined search patterns can be applied to spans (preferably excludingmetadata spans) to identify search chunks that can be used as searchterms to search a reference database (e.g., reference database 230).

For example, one or more search patterns can be defined (e.g., usingregex) to search a given span for words and/or phrases that can indicatethat a reference may follow. By way of nonlimiting example, a ReferencesMatch Detector Regular Expression Pattern can be defined, such as:

(?i)((example(.*)(:|is|be)|examples(.*)(:|are|be)|\\brefs\\b(.*)(:|are|be)|\\bref(.*)(\\.|:|is)|resemble(.*)(:|is|are|be|)|reference(.*)(:|is|are|be)|references(.*)(:|are|be)|(\\bmood\\b|\\bmoods\\b)(?!.*(is|are|(sh|w|c)ould|will))|(\\bsong\\b|\\bsongs\\b|\\bmusic\\b|\\btrack\\b|\\bguitar\\b|\\bdrums\\b|\\binstrumental\\b|\\binstrumentals\\b)(.*)(like|likeis|like are|liking|love|love is|love are|such as|similar to|ala|ala|similar (feel|vein) to|:)|( song is | songs are | especially the song| especially the songs | including the song | including the songs |reference song | sound:| should sound (.*) like:)|((like|love|adore)(the| a| this| that| those| these)( song| songs| track| music|tracks))|(syncfloor:youtubelinktranslation:)|something like(:| )|maybelike(:| )|(replacing:|replace:|replace |replacing|replacementfor:|replacement for|replacements for:|replacements for|to replace thesong|to replace))\\s*\\n*)Accordingly, reference identification component 320 can apply one ormore search patterns to identify spans that may contain references.

Generally, spans (e.g., spans matching the References Match Detectorsearch pattern) are analyzed to identify one or more search chunks thatcan be used as search terms to search a reference database for acorresponding reference. Spans can be analyzed as a whole and/or insegments. For example, because natural language inputs can include listsof references appearing across multiple lines of text, in someembodiments, an identified span (e.g., based on one or more searchpatterns) can be partitioned by carriage return to separate out multiplelines. Lines can be skipped, for example, if a line includes metadatawithout music content (e.g., deadline, budget) or if the line is empty.Remaining lines can be analyzed to identify a search chunk from theline. In some embodiments (e.g., in the event that a given span does notinclude a carriage return), the entire span can be analyzed to identifya search chunk from the span.

In some embodiments, one or more markers can be defined to indicate aconfidence that a reference will follow. For example, high confidencereference markers can be defined to indicate a high likelihood that areference and/or reference search terms will appear after a marker. Byway of nonlimiting example, a highConfidenceMarkers pattern can bedefined, such as:

{   “syncfloor:youtubelinktranslation:”,   “song like”,   “Song like”,  “references:”,   “reference:”,   “References:”,   “Reference:”,  “references :”,   “reference :”,   “References :”,   “Reference :”,  “REFERENCE:”,   “REFERENCE TRACK:”,   “REFERENCE :”,   “REFS:”,  “REFS :”,   “REF:”,   “REF :”,   “examples:”,   “example:”,  “Examples:”,   “Example:”,   “examples :”,   “example :”,   “Examples:”,   “Example :”,   “EXAMPLE:”,   “EXAMPLE :”,   “EXAMPLES:”,  “EXAMPLES :”,   “sound:”,   “reference is:”,   “reference is ”,  “reference are ”,   “references is ”,   “references are ”,  “references are:”,   “reference song ”,   “example is ”,   “exampleare ”,   “examples is ”,   “examples are ”,   “ looking for is ”,   “likes:”,   “ like:”,   “ are:”,   “ is:”,   “ be:”,   “ take:”,   “below:”,   “ after:”,   “ below -”,   “ genre:”,   “ for:”,   “ song:”,  “ songs:”,   “ SONG:”,   “ SONGS:”,   “ track:”,   “ tracks:”,   “TRACK:”,   “ TRACKS:”,   “ ref. ”,   “ resembles ”,   “ resemble ”,  “replace the song ”,   “replace ”,   “replacements for ”,  “replacement for ”,   “replacing: ”,   “replace: ”,   “Replacementsfor ”,   “Replacement for ”,   “Replacing: ”,   “Replacing ”,  “replacements ”,   “replacement ”,   “ such as the reference song ”,  “ such as the reference ”,   “ such as the song ”,   “ such as ”,   “especially the song ”,   “ especially the songs ”,   “ including thesong ”,   “ including the songs ”,   “ liking is ”,   “ liking are ”,  “ liking ”,   “ likes is ”,   “ likes are”,   “ like is ”,   “ likeare ”,   “like the song ”,   “love the song ”,   “love with the song ”,  “adore the song ”,   “like that song ”,   “love that song ”,   “adorethat song ”,   “like this song ”,   “love this song ”,   “adore thissong ”,   “like a song ”,   “love a song ”,   “ love with a song ”,  “adore a song ”,   “like the music ”,   “love the music ”,   “lovewith the music ”,   “adore the music ”,   “like that music ”,   “lovethat music ”,   “like the songs ”,   “love the songs ”,   “ love withthe songs ”,   “adore the songs ”,   “like these songs ”,   “love thesesongs ”,   “adore these songs ”,   “like the track ”,   “love the track”,   “ love with the track ”,   “adore the track ”,   “like that track”,   “love that track ”,   “adore that track ”,   “like this track ”,  “love this track ”,   “adore this track ”,   “like a track ”,   “lovea track ”,   “ love with a track ”,   “adore a track ”,   “like thetracks ”,   “love the tracks ”,   “ love with the tracks ”,   “adore thetracks ”,   “like these tracks ”,   “love these tracks ”,   “adore thesetracks ”,   “ wants are ”,   “ wants is ”,   “ want are ”,   “ want is”,   “ like very much is ”,   “ likes very much is ”,   “ a la ”,   “ala ”,   “ similar to ”,   “Music like ”,   “music like ”,  “Instrumental like ”,   “instrumental like ”,   “Instrumentals like ”,  “instrumentals like ”,   “ similar feel to ”,   “ similar vein to ”,  “something like”,   “Something like”,   “maybe like”,   “Maybe like”,  “guitar like ”,   “Guitar like ”,   “drums like ”,   “Drums like ”  }

Accordingly, reference identification component 320 can identify theearliest occurrence of a high confidence marker in a given span or line,for example, by applying one or more corresponding search patterns. Ifone of the markers is found in a given span or line, the span or linecan be broken at the earliest marker, and the text that follows can beidentified as the search chunk. In the case of a span partitioned intolines (e.g., in the event the span includes a list of references), anidentified marker is preferably stored for future reference whileprocessing the remaining lines.

Additionally and/or alternatively, low confidence reference markers(e.g., is, like, etc.) can be defined that indicate a relatively lowerconfidence that the subsequent characters will be a reference and/orreference search terms. Since such terms may be relatively common, oneway to increase the confidence is to define one or more triggers (e.g.,example, mood, song) and only extract a search term when a triggerappears and a corresponding marker (preferably the earliest marker in agiven span or line) appears after the trigger. For example, alowConfidenceMarkerTriggers pattern can be defined, such as:

{  “reference”,  “example”,  “mood”,  “song” }Likewise, a lowConfidenceMarkers pattern can be defined, such as:

{  “ like ”,  “ of is ”,  “ for is ”,  “ is ”,  “ are ”,  “ be ” }

Accordingly, reference identification component 320 can identify atrigger and a subsequent marker (e.g., the earliest low confidencemarker appearing after the trigger), for example, by applying one ormore corresponding search patterns. If a trigger and marker are found ina given span or line, the span or line can be broken at the marker, andthe text that follows can be identified as the search chunk. In the caseof a span partitioned into lines (e.g., in the event the span includes alist of references), an identified marker is preferably stored forfuture reference while processing the remaining lines.

In some embodiments, identified search chunks can be evaluated todetermine whether a search chunk includes text suggesting a falsepositive, in which case the search chunk can be skipped. For example, atrigger and marker combination such as “songs . . . such as . . . ” mayhave matched an unintended phrase such as “songs which are made withinstruments such as guitar . . . ” To address such false positives, aReferences Skip Detector Regular Expression Pattern can be defined, suchas:

(?i)(made with|made out of|going to be played|song combined with|song isbeing|song underlining|already well-known|unreleased|mid-tempo|orsomething like that|composed with|composed using|composed of|describedas|edited in|likes about this|song needs to appeal|song mustappeal|lyrical themes|about love|song about|songs about|use ofsounds|used for sampling|mix of instruments|all- in|localcable|specifications:|specs:|note:|weird instrumentation|from artists|etc|references so)Accordingly, search chunks with text that match a defined skip searchpattern can be identified as false positives, and the search chunksskipped.

In this manner, one or more search patterns can be used to identify oneor more search chunks from a given span. In some embodiments, areference database is searched for an identified search chunk at thetime the search chunk is identified. In this sense, markers arepreferably searched in a defined priority order to increase thelikelihood of locating a meaningful search chunk faster and with reducedprocessing. In other embodiments, any number of search chunks (e.g., allof them) can be identified before searching any of them.

As described above, a reference database (e.g., reference database 230)can be searched using an identified search chunk as a search term. Insome embodiments, reference identification component 320 can callreference search component 260 to search reference database 230, asdescribed above. If the search does not produce any results, one or moresubsequent search chunks can be identified, and a corresponding searchperformed. For example, multiple search chunks may be identified from agiven span based on matching multiple markers and/or marker/triggercombinations. Additionally and/or alternatively, one or more searchchunks can be identified using one or more delimiters (e.g., commas,“and”s, “or”s, etc.). For example, if the number of delimiters appearingin a given span, line and/or search chunk is greater than two, this mayindicate the presence of multiple songs separated by a delimiter.Accordingly, one or more search chunks can be allocated by splitting thespan, line and/or search chunk by delimiter, and performingcorresponding searches (e.g., searching reference database 230 by song).If a reference is located, reference identification component 320 cancreate a corresponding reference span (e.g., including a correspondingvalence categorization) and store the identified reference. For example,if a search chunk was identified from a span that was previouslycategorized as positive, the reference span can be categorized aspositive (e.g., Positive). Likewise, if a search chunk was identifiedfrom a span that was previously categorized as negative, the referencespan can be categorized as negative (e.g., Negative). If a reference isnot located, a reference span can be identified as unresolved. In thismanner, spans and/or lines (e.g., each line of each span, excludingmetadata spans) can be searched for references. Other variations ofsearch methodologies can be implemented within the scope of the presentdisclosure.

In some instances, an identified reference span may encompass multiplespans that sentence identification component 340 had identified andvalence generator 310 had assigned a valence categorization.Accordingly, in some embodiments, reference identification component 320removes or redacts spans that occur within an identified reference span.

In this manner, annotation component 300 analyzes a raw input, segmentsthe input into spans, identifies references, and assigns valencecategorizations. An annotated input can be generated (e.g., byannotation generator 330) comprising one or more documents and/or datastructures. For example, an annotated input can be implemented using atagged text span data structure to describe a given span. Table 4describes an example tagged text span data structure with examplefields. For example, fields can be designated for start & stop characternumbers for a given span, identification of spans that include areference or a hyperlink, and a characterization of the sentiment of aparticular span (e.g., a valence categorization). Other variations ofdata structures and/or fields can be implemented within the presentdisclosure.

TABLE 4 Field Type Description startChar int Zero based offset of firstcharacter in the raw input associated with this span endChar int Zerobased offset of last character in the raw input associated with thisspan valence Valence Categorization of the sentiment of a particularspan. Values can correspond to defined valence levels. referenceTypeReferenceType Can be set (e.g., with values for “artist” and “song”) ornot set. If set, this span is a reference span for a correspondingReference Type (e.g., artist, song, etc.). If not set, then this span isa statement from the raw input. hyperlink String Can be set with a URIidentified from this span. Identifier String An additional identifierfor the content target of the URI if available e.g. ISRC for a song

As such, annotation generator 330 can generate an annotated input usinga tagged text span data structure to describe spans, references, andvalence categorizations for a raw input. In some embodiments, annotationgenerator 330 generates one or more documents compiling the raw input,corresponding spans, span information, portions thereof and/orcombinations thereof. Terms metadata can be separated from the rawinput, e.g., except where the terms metadata includes content (e.g.,title or description) that comprises the entire raw input. By way ofnonlimiting example, annotation generator 330 can generate a document(e.g., in JSON format), such as the following annotated input. Thisexample document is merely meant as an example. Other variations ofdocuments, data structures and/or corresponding fields may beimplemented within the present disclosure.

{  “content”: {   “language”: “en”,   “value”: “Organic and Folkyrhythmic song for trade show video\nFor a trade show video (selfpromotion as the brand is a trade show) a client is looking for anorganic and folky song that has a nice driving rhythm. We prefer scarcevocals or instrumentals. The song shall convey a good drive and bepositive without being kitschy. The film will be around 2:30 mins long.References are:\nMatthew & The Atlas - I will remain\nRadical Face -Wrapped in Piano Strings\nLicense:\nMedia:\tInternet, Trade show/Events\nTerritory:\tWorld\nTerm:\t72 months\nPayout:\tEUR 4,000 (all-in)\nDeadline:\t28.04.2017 13:00 CET”  },  “offerid”:“66153b39-c2d4-4a33-8454-4d7be1c78c43”,  “contenthash”:“egWqAPR+R3hHiCbzfEaoMQ==”,  “originatinghost”:“Kirts-MacBook-Pro.local”,  “metadata”: {   “spans”: [    {     “class”:“text”,     “locateby”: “position”,     “start”: 452,     “end”: 460   },    {     “class”: “text”,     “locateby”: “position”,     “start”:461,     “end”: 496    },    {     “class”: “text”,     “locateby”:“position”,     “start”: 497,     “end”: 513    },    {     “class”:“text”,     “locateby”: “position”,     “start”: 514,     “end”: 529   },    {     “class”: “text”,     “locateby”: “position”,     “start”:530,     “end”: 556    },    {     “class”: “text”,     “locateby”:“position”,     “start”: 557,     “end”: 586    }   ] ,   “class”:“emailbodyheaders”,   “firstsentence”: “Organic and Folky rhythmic songfor trade show video”  },  “annotations”: {   “spans”: [    {    “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 0,     “end”: 296    },    {    “class”: “statement”,     “valence”: “Negative”,     “locateby”:“position”,     “start”: 297,     “end”: 318    },    {     “class”:“statement”,     “valence”: “HighlyPositive”,     “locateby”:“position”,     “start”: 319,     “end”: 375    },    {     “class”:“reference”,     “valence”: “Positive”,     “start”: 376,     “end”:410,     “locateby”: “uri”,     “uri”:“spotify:track:52VPecMGJQOxyWWWgnUvsI”,     “isrc”: “GB6TW1000022”,    “embed”:“http://open.spotify.com/embed?uri=spotify:track:52VPecMGJQOxyWWWgnUvsI”   },    {     “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 411,     “end”: 411    },    {    “class”: “reference”,     “valence”: “Positive”,     “start”: 412,    “end”: 450,     “locateby”: “uri”,     “uri”:“spotify:track:0r7EiYTNNP0WCzcaefN6TZ”,     “isrc”: “DEX260606906”,    “embed”:“http://open.spotify.com/embed?uri=spotify:track:0r7EiYTNNP0WCzcaefN6TZ”   },    {     “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 451,     “end”: 451    }   ]  }}

Turning now to FIG. 4, FIG. 4 illustrates exemplary translationcomponent 400 (which may correspond to translation component 280 of FIG.2). In some embodiments, translation component 400 parses structureddata from the content of an annotated input to generate build terms(e.g., deadline, budget, catalogs to include/exclude, media, territory,term, etc.). Generally, translation component 400 utilizes the annotatedinput to generate features in the schema corresponding to the annotatedinput. In the embodiment illustrated by FIG. 4, translation component400 includes feature map generator 410 and fuzz component 440. Featuremap generator 410 generates a feature map associating valencecategorizations of the annotated input with corresponding identifiedschema features. Fuzz component 440 applies fuzzing to the identifiedschema features to reduce the risk of conflicts and increase therelevance of search results. In this manner, translation component 400translates an annotated input into corresponding schema features. Thegenerated features can then be used to generate a query string.

Generally, feature map generator 410 processes an annotated input byparsing it into its constituent spans, generating corresponding schemafeatures for each span, and compiling the generated features into one ormore data structures such as a feature map. For spans that indicateresolved references (e.g., songs), feature map generator 410 looks upcorresponding schema features (e.g., categories & category instances)from one or more indexes and/or databases (e.g., reference database230). For spans that indicate statements from the raw input, feature mapgenerator 410 identifies schema features for the spans using naturallanguage processing. Feature map generator 410 can accomplish this usingmusical feature extractor 420 and artist mention musical featuregenerator 430.

Musical feature extractor 420 extracts mentions of musicalcharacteristics in a given span and translates those musicalcharacteristics to musical features of the schema. For example, musicalfeature extractor 420 can generate forward Ngrams (e.g., min 2, max 8)from a given span, and lookup the Ngrams in a dictionary that translatesnatural language to musical features of the schema (e.g., dictionary210). For any matched Ngram, corresponding translated features can bestored. For example, translated features and corresponding valencecategorizations can be stored in a feature map. Tokens can be utilizedto keep track of which words in a span have been translated. Forexample, a token can be applied to each word in a span, and the tokenscan be enabled by default. If an Ngram is matched, the tokens for eachword in the Ngram can be disabled. For any remaining enabled tokens,corresponding individual words can be looked up in the dictionary toidentify corresponding features. For any match, the correspondingmusical feature and valence categorization are stored in the feature map(e.g., organized by valence level and/or schema category). In thismanner, a feature map can be populated with translated featurescorresponding to musical characteristics in a given span.

In some embodiments, artist mention musical feature generator 430 usesremaining enabled tokens to search a reference database for artistmentions, retrieve corresponding musical features of the schema, andstore the features in the feature map. As a preliminary matter, if aspan being processed matched a defined reference search pattern (e.g.,as described above), the span can be skipped (for example, because thereference database was already searched for matches from the span). Fora given span to be processed, artist mention musical feature generator430 can run a defined artist mention search pattern (e.g., on the fullspan, or a portion thereof) to identify the location of content in thespan to process. For example, if a search pattern identifies an artistmention marker in a span, the span can be broken at the marker, and thetext that follows can be used for processing. As such, artist mentionmusical feature generator 430 can perform one or more delimited searchesto search for artist mentions in the identified text (e.g., afterremoving disabled tokens).

For example, artist mention musical feature generator 430 can perform acomma delimited search by removing disabled tokens (excluding commas),splitting the remaining text at any comma to generate segments of acomma delimited sentence, and searching a reference database for artiststhat match a segment of the comma delimited sentence. If a search hits,corresponding musical features for that artist can be retrieved (e.g.,from the reference database), the features can be stored (e.g., in afeature map organized by valence categorizations and/or schemacategory), and the tokens corresponding to the matched artist can bedisabled to remove the matched artist from the comma delimited sentence.

Additionally and/or alternatively, artist mention musical featuregenerator 430 can perform a space delimited search, for example, byreconstituting the remaining comma delimited sentence into a spacedelimited sentence, generating forward Ngrams (e.g., min 1, max 4), andsearching the reference database for artists matching the Ngrams. If asearch hits, corresponding musical features for that artist can beretrieved (e.g., from the reference database), the features can bestored (e.g., in a feature map organized by valence categorizationsand/or schema categories), and the tokens corresponding to the matchedartist can be disabled to remove the matched artist from the spacedelimited sentence.

Accordingly, feature map generator 410 can identify musical featuresfrom a given span, and store the identified musical features in one ormore data structures such as a feature map. Each of the spans from anannotated input can be processed in this manner to generate a featuremap that includes translated music features from a natural languagedescription of music. Preferably, duplicate features are removed.

Fuzz component 440 applies fuzzing to the identified musical features(e.g., the feature map) to reduce the risk of conflicts and to increasethe relevance of search results based on the feature map. For example,fuzz component 440 can include an intra valence fuzz component, an intervalence fuzz component, an inter valence negation correction componentand/or an inter valence copy component.

The intra valence fuzz component identifies related features in theschema that may describe overlapping content. For example, in a categorysuch as vibe, features such as rhythmic and danceable may be consideredinterchangeable in some respects. When either feature is present, it maybe preferable to search for results matching either feature.Accordingly, if one of a defined group of similar features is includedin a given valence level in the feature map, the intra valence fuzzcomponent includes the other feature(s) in the group, as well.

The inter valence fuzz component addresses feature categories that cancreate significant false negatives. For example, a raw input may haverequested uptempo music, but also included references that were midtempoand/or downtempo. If an input has such a conflict, the inter valencefuzz component can reduce the resulting impact by “fuzzing” theconflicting features across valence levels. More specifically, aconflicting feature from one valence level can be added to the valencelevel with the other conflicting feature. For example, the inter valencefuzz component can identify defined conflicts between a positive musicalfeature for a reference (e.g., midtempo and downtempo in the Positivevalence) and a positive musical feature derived from a sentence (e.g.,uptempo in the HighlyPositive valence), and resolve conflicts by addingthe positive musical feature for the reference to the valence level withthe positive musical features derived from the sentence (e.g., byplacing the midtempo and downtempo features in the HighlyPositivevalence).

The inter valence negation correction component addresses situationswhere the feature map includes a given feature in both positive andnegative valences. For example, a feature map that includes avocals_with_music feature from the production category in both apositive category (e.g., because a reference included vocals) and anegative category (e.g., because a negative sentiment was erroneouslyimplied based on natural language processing). For some partitionedcategories, this type of conflict can prevent any search results fromreturning. Accordingly, the inter valence negation correction componentcan identify defined conflicts between a positive musical feature for areference (e.g., in the Positive valence) and a negative musical featurederived from a sentence (e.g., in the HighlyNegative valence), andresolve identified conflicts by removing the negative musical featurefrom one of the valence levels (e.g., removing the feature derived fromthe sentence).

The inter valence copy component addresses defined situations where afeature derived from a sentence (e.g., in the HighlyPositive valence) isincompatible with a feature for a reference (e.g., in the Positivevalence). For example, for some categories such as genre, it may bepossible to imply genres from references that are incompatible withgenres explicitly included in a description. Accordingly, the intervalence copy component can identify at least one defined category forcopying, and copy positive musical features for that category that wereextracted from a sentence (e.g., in the HighlyPositive Valence) into thevalence level for the musical features implied from references (e.g., inthe Positive valence), and/or vice versa.

In this manner, translation component 400 can generate a set oftranslated features from an annotated input. In some embodiments,translation component 400 generates one or more documents compiling theraw input, corresponding spans, span information, structured data (e.g.,build terms), valence-ordered feature map, portions thereof and/orcombinations thereof. By way of nonlimiting example, translationcomponent 400 can generate a document (e.g., in JSON format), such asthe following. This example document is merely meant as an example.Other variations of documents, data structures and/or correspondingfields may be implemented within the present disclosure.

{  “content”: {   “language”: “en”,   “value”: “Organic and Folkyrhythmic song for trade show video\nFor a trade show video (selfpromotion as the brand is a trade show) a client is looking for anorganic and folky song that has a nice driving rhythm. We prefer scarcevocals or instrumentals. The song shall convey a good drive and bepositive without being kitschy. The film will be around 2:30 mins long.References are:\nMatthew & The Atlas - I will remain\nRadical Face -Wrapped in Piano Strings\nLicense:\nMedia:\tInternet, Trade show/Events\nTerritory:\tWorld\nTerm:\t72 months\nPayout:\tEUR 4,000 (all-in)\nDeadline:\t28.04.2017 13:00 CET”  },  “offerid”:“66153b39-c2d4-4a33-8454-4d7be1c78c43”,  “contenthash”:“egWqAPR+R3hHiCbzfEaoMQ==”,  “originatinghost”:“Kirts-MacBook-Pro.local”,  “metadata”: {   “spans”: [    {     “class”:“text”,     “locateby”: “position”,     “start”: 452,     “end”: 460   },    {     “class”: “text”,     “locateby”: “position”,     “start”:461,     “end”: 496    },    {     “class”: “text”,     “locateby”:“position”,     “start”: 497,     “end”: 513    },    {     “class”:“text”,     “locateby”: “position”,     “start”: 514,     “end”: 529   },    {     “class”: “text”,     “locateby”: “position”,     “start”:530,     “end”: 556    },    {     “class”: “text”,     “locateby”:“position”,     “start”: 557,     “end”: 586    }   ] ,   “class”:“emailbodyheaders”,   “firstsentence”: “Organic and Folky rhythmic songfor trade show video”  },  “annotations”: {   “spans”: [    {    “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 0,     “end”: 296    },    {    “class”: “statement”,     “valence”: “Negative”,     “locateby”:“position”,     “start”: 297,     “end”: 318    },    {     “class”:“statement”,     “valence”: “HighlyPositive”,     “locateby”:“position”,     “start”: 319,     “end”: 375    },    {     “class”:“reference”,     “valence”: “Positive”,     “start”: 376,     “end”:410,     “locateby”: “uri”,     “uri”:“spotify:track:52VPecMGJQOxyWWWgnUvsI”,     “isrc”: “GB6TW1000022”,    “embed”:“http://open.spotify.com/embed?uri=spotify:track:52VPecMGJQOxyWWWgnUvsI”   },    {     “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 411,     “end”: 411    },    {    “class”: “reference”,     “valence”: “Positive”,     “start”: 412,    “end”: 450,     “locateby”: “uri”,     “uri”:“spotify:track:0r7EiYTNNP0WCzcaefN6TZ”,     “isrc”: “DEX260606906”,    “embed”:“http://open.spotify.com/embed?uri=spotify:track:0r7EiYTNNP0WCzcaefN6TZ”   },    {     “class”: “statement”,     “valence”: “HighlyPositive”,    “locateby”: “position”,     “start”: 451,     “end”: 451    }   ] },  “analysis”: {   “metadata”: {    “Format”: “SyncFloorHeaders”,   “Title”: “Organic and Folky rhythmic song for trade show video”,   “Deadline”: “Fri, 28 Apr 2017 05:00:00 −0700”,    “BudgetLow”:“4000”,    “BudgetHigh”: “4000”,    “BudgetCurrency”: “EUR”,   “ReleaseDateRange”: [ ],    “CatalogInclude”: [ ],   “CatalogExclude”: [ ],    “SearchResultMinBar”: “1.0”,    “Media”: [    “Internet, Trade show/ Events”    ],    “Territory”: [     “World”   ],    “Term”: [     “72 months”    ],    “Segment”: [ ]   },  “featuremap”: {    “valences”: [     {      “valence”:“HighlyPositive”,      “features”: [       {        “featureType”:“Vibe”,        “featureValues”: “rhythmic, danceable, happy”       },      {        “featureType”: “Production”,        “featureValues”:“vocals_with_music, instrumental”       },       {        “featureType”:“Arrangement”,        “featureValues”: “organic_acoustic_or_electric,acoustic”       },       {        “featureType”: “Tempo”,       “featureValues”: “midtempo, uptempo”       },       {       “featureType”: “Genre”,        “featureValues”: “americana,tsf_exclusive_Folk, tsf_exclusive_folk”       },       {       “featureType”: “Annotations”,        “featureValues”: “drivingrhythm, Organic, Folky, rhythmic, organic, folky, scarce vocals,instrumentals, good drive, positive”       },       {       “featureType”: “Modifiers”,        “featureValues”:“instrumental_stem_available”       }      ]     },     {     “valence”: “Positive”,      “features”: [       {       “featureType”: “Vibe”,        “featureValues”: “danceable,dynamic, reflective, rhythmic”       },       {        “featureType”:“Production”,        “featureValues”: “clean, vocals_with_music, studio,instrumental”       },       {        “featureType”: “Arrangement”,       “featureValues”: “organic_acoustic_or_electric,organic_electric_or_electronic, electric_or_electronic”       },       {       “featureType”: “Shape”,        “featureValues”:“calm_intro_or_fade_in, calm_outtro_or_fade_out, multiple_crescendos”      },       {        “featureType”: “Tempo”,        “featureValues”:“midtempo, uptempo”       },       {        “featureType”: “Popularity”,       “featureValues”: “known, popular”       },       {       “featureType”: “Genre”,        “featureValues”: “chamber pop,folk-pop, indie anthem-folk, indie folk, indiecoustica, neo mellow, newamericana, stomp and holler, indie pop, modern rock, americana,tsf_exclusive_folk”       },       {        “featureType”:“Annotations”,        “featureValues”: “matthew and the atlas, an artiston spotify, jacksonville beach, florida-based radical face is primarilyben cooper, who is also one-half of similar quiet-is-the-new-loud duoelectric president.”       },       {        “featureType”:“RelatedArtists”,        “featureValues”: “matthew and the atlas,benjamin francis leftwich, roo panes, johnny flynn, the paper kites,james vincent mcmorrow, dustin tebbutt, sons of the east, nathanielrateliff, horse feathers, bear's den, luke sital-singh, beta radio, stularsen, fionn regan, little may, dry the river, s. carey, donovan woods,gregory alan isakov, blind pilot, radical face, electric president, seawolf, noah and the whale, the tallest man on earth, rogue wave, aleximurdoch, freelance whales, milo greene, margot & the nuclear so andso's, edward sharpe & the magnetic zeros, the oh hellos, volcano choir,william fitzsimmons”       }      ]     },     {      “valence”:“Neutral”,      “features”: [       {        “featureType”: “Release”,       “featureValues”: “contemporary, 2010_and_later, 00s”       }     ]     },     {      “valence”: “Negative”,      “features”: [      {        “featureType”: “Vibe”,        “featureValues”:“emotional, quirky”       },       {        “featureType”: “Tempo”,       “featureValues”: “downtempo”       },       {       “featureType”: “Genre”,        “featureValues”: “downtempo,melancholia, slow core, emo”       },       {        “featureType”:“Annotations”,        “featureValues”: “kitschy”       }      ]     },    {      “valence”: “HighlyNegative”,      “features”: [ ]     }    ]  },   “mapreferences”: [    {     “id”:“1df2916c-eaf8-4ef9-a172-d26f44334fa0”,     “catalogId”:“tsfreferencemapcatalogid”,     “songName”: “I Will Remain”,    “searchableSongName”: “Will Remain”,     “isrc”: “GB6TW1000022”,    “owner”: “”,     “groupOwners”: “”,     “songPreviewUrl” :“http://open.spotify.com/embed?uri=spotify:track:52VPecMGJQOxyWWWgnUvsI”,    “songArtUrl”:“https://i.scdn.co/image/633249e04849b2463033c9165131d2dcb479a61c”,    “songDuration”: 206546,     “artistName”: “Matthew And The Atlas”,    “artistUrl”:“http://open.spotify.com/embed?uri=spotify:artist:0lSENl3bteP8p2NbiSP7RM”,    “albumName”: “To the North”,     “searchableAlbumName”: “To theNorth”,     “albumUrl”:“http://open.spotify.com/embed?uri=spotify:album:0cKaHI9t8EnlykFdIRspw3”,    “releaseDate”: “2010-04-11”,     “releaseDatePrecision”: “day”,    “cSF”: “danceable, dynamic, reflective, clean, vocals_with_music,studio, organic_acoustic_or_electric, calm_intro_or_fade_in,calm_outtro_or_fade_out, multiple_crescendos, midtempo, known,contemporary, 2010_and_later”,     “gF”: “chamber pop, folk-pop, indieanthem-folk, indie folk, indiecoustica, neo mellow, new americana, stompand holler”,     “aF”: “Matthew And The Atlas, an artist on Spotify”,    “iF: “”,     “raF”: “Matthew And The Atlas, Benjamin FrancisLeftwich, Roo Panes, Johnny Flynn, The Paper Kites, James VincentMcMorrow, Dustin Tebbutt, Sons Of The East, Nathaniel Rateliff, HorseFeathers, Bear's Den, Luke Sital-Singh, Beta Radio, Stu Larsen, FionnRegan, Little May, Dry the River, S. Carey, Donovan Woods, Gregory AlanIsakov, Blind Pilot”,     “lyrics”: “”,     “score”: 0,    “thematicHighlights”: “”    },    {     “id”:“587626b4-62aa-4c80-87e0-898a9802b71e”,     “catalogId”:“tsfreferencemapcatalogid”,     “songName”: “Wrapped In Piano Strings”,    “searchableSongName”: “Wrapped In Piano Strings”,     “isrc”:“DEX260606906”,     “owner”: “”,     “groupOwners”: “”,    “songPreviewUrl”:“http://open.spotify.com/embed?uri=spotify:track:0r7EiYTNNP0WCzcaefN6TZ”,    “songArtUrl”:“https://i.scdn.co/image/cc2e484ac2102ccb73bb45d0ed1aa7d141809598”,    “songDuration”: 216613,     “artistName”: “Radical Face”,    “artistUrl”:“http://open.spotify.com/embed?uri=spotify:artist:5EM6xJN2QNk0cL7EEm9HR9”,    “albumName”: “Ghost”,     “searchableAlbumName”: “Ghost”,    “albumUrl”:“http://open.spotify.com/embed?uri=spotify:album:0VYi6aRMwxXpfvNwDCr3bB”,    “releaseDate”: “2007”,     “releaseDatePrecision”: “year”,    “cSF”: “rhythmic, dynamic, reflective, clean, vocals_with_music,studio, organic_electric_or_electronic, calm_intro_or_fade_in,calm_outtro_or_fade_out, multiple_crescendos, uptempo, midtempo, known,popular, contemporary, 00s”,     “gF”: “chamber pop, folk-pop, indiefolk, indie pop, modern rock, stomp and holler”,     “aF”: “JacksonvilleBeach, Florida-based Radical Face is primarily Ben Cooper, who is alsoone-half of similar quiet-is-the-new-loud duo Electric President.”,    “iF”: “”,     “raF”: “Radical Face, Electric President, Blind Pilot,Sea Wolf, Horse Feathers, Noah And The Whale, S. Carey, The Tallest ManOn Earth, Matthew And The Atlas, Rogue Wave, Alexi Murdoch, Gregory AlanIsakov, Freelance Whales, Benjamin Francis Leftwich, Milo Greene, Margot& The Nuclear So And So's, Dry the River, Edward Sharpe & The MagneticZeros, The Oh Hellos, Volcano Choir, William Fitzsimmons”,     “lyrics”:“”,     “score”: 0,     “thematicHighlights”: “”    }   ]  } }

Having translated a natural language description into a common language(e.g., musical features of a music description and categorizationschema), a search query can be generated from the translated featuresand a search can be performed to identify potential matches. Anysearchable index can be used as the target of the search (e.g., catalogindex 220, one or more ingested third party catalogs, one or moreexternal third party catalogs, etc.). In embodiments that search catalogindex 220, a corpus of potentially millions of songs over many ingestedcatalogs may be searched. Various full text search engines can beimplemented using custom schemas, as would be understood by a person ofordinary skill in the art. Accordingly, a matchmaking system can beimplemented using query generation, result ranking and resultsfiltering.

Turning now to FIG. 5, FIG. 5 illustrates exemplary matchmakingcomponent 500 (which may correspond to matchmaking component 290 of FIG.2). Matchmaking component 500 includes query string generator 510,search execution component 520 and ranking and statistics component 530.Generally, query string generator 510 generates a query string from aset of schema features (e.g., using the feature map), search executioncomponent 520 executes a search on a defined index using the generatedquery string, and ranking and statistics component 530 performs ananalysis of search results to facilitate efficient user review.

Query string generator 510 can generate a query string from a set ofschema features to be searched (e.g., translated and stored in a featuremap), for example, by generating query terms for corresponding schemacategories and/or valence categorizations using one or more query termprofiles. Generally, a query term profile is a collection of informationthat can be used to generate query terms. A query term profile (e.g.,query term profiles 240 a through 240 n in FIG. 2) can be defined withquery term generators (e.g., query term generators 242) for schemacategories, schema features and/or valence levels. A query termgenerator may include a set of term generation rules and/or a query termshell that can be populated using defined query term characteristics forschema categories, schema features and/or valence levels in order toemit specific instances of query terms during query generation. Forexample, in a music description and categorization schema, a query termgenerator for an Arrangement category can generate a query term formusical features to be searched from that category (e.g.,arrangement:general:acoustic). In this example, the query term generator(i) uses query term characteristics for the Arrangement category definedin a query term profile and (ii) uses the musical feature to be searched(e.g., acoustic) as a keyword. Additionally and/or alternatively, aquery term generator for a particular valence level can generate a queryterm for musical features that were assigned to that valence level usingquery term characteristics for that valence level in the query termprofile. In this manner, query terms can be generated for a set offeatures to be searched and a query string constructed from the queryterms.

Query terms can be generated using query term characteristics for schemacategories, schema features and/or valence levels that can be defined tofacilitate generation of query terms. Query term characteristics caninclude characters for the query term, boost multipliers, outer prefixcontrol, and the like. Boost multipliers can be set to define therelative importance of schema categories, schema features and/or valencelevels using score multiplication factors for given categories, featuresand/or valences, as explained in more detail below with respect toranking and statistics component 530. An outer prefix control can be setto define whether a query term for a particular schema category, schemafeature and/or valence level is a “hard” filter (i.e., a search resultmust match at least one keyword for a particular schema category, schemafeature and/or valence categorization) or a “soft” filter (a searchresult need not match a keyword for a particular schema category, schemafeature and/or valence categorization, but scoring may be affected).Table 5 describes example query term characteristics that can be definedfor a query term profile. In some embodiments, additional query termprofiles can be defined to produce alternative search constraints. Forexample, one query term profile can be defined using a primary set ofboost multipliers, and a second query term profile can be defined usinga wildcard set of boost multipliers. In the embodiment depicted in FIG.2, query term profile 240 a can be designated as a primary profile,including query term generators 242, boost multipliers 244 and outerprefix controls 246. Meanwhile, query term profile 240 n can bedesignated as a wildcard profile with relaxed search criteria in orderto expand the universe of search results.

TABLE 5 Primary Wildcard Open Close Boost Boost Type Tag Prefix MarkMark Multiplier Multiplier Filter Valence HighlyPositive + ( ) 10 10Hard Valence Positive + ( ) 5 5 Hard Valence Neutral N/A ( ) 1 1 HardValence Negative − ( ) 5 5 Hard Valence HighlyNegative − ( ) 10 10 HardFeature Vibe cSF: “ ” 14 13 Hard Feature Arrangement cSF: “ ” 10 9 SoftFeature Shape cSF: “ ” 8 7 Soft Feature Themes tF: “ ” 7 11 Soft FeatureTranslatedThemes tF: “ ” 4 4 Soft Feature Tempo cSF: “ ” 4 4 HardFeature Production cSF: “ ” 4 4 Hard Feature Popularity cSF: “ ” 2 2Soft Feature Release cSF: “ ” 2 2 Hard Feature Genre gF: “ ” 1.2 1 HardFeature Annotations aF: “ ” 1 7 Soft Feature Instruments iF: “ ” 1 1Soft Feature RelatedArtists raF: “ ” 1 1.5 Soft ExclusionGenreExclusions −gF: “ ” 1.2 1 Hard

In an exemplary embodiment, query string generator 510 generates a querystring from a set of schema features to be searched (e.g., from afeature map) using a defined query term profile. Preferably, a featuremap is organized by valence level (e.g., so all the features categorizedin a Highly Positive valence level can be processed together, all thefeatures categorized in a Positive valence level can be processedtogether, etc.). For each valence level appearing in the feature map,query string generator 510 accesses a query term generator for thevalence level (skipping valence levels undefined in the correspondingquery term profile). For each schema category of the features appearingin that valence level in the feature map, query string generator 510accesses a query term generator for the schema category. In this manner,query string generator 510 generates query terms for each featureappearing in the feature map based on query term generators forcorresponding schema categories and valence levels.

In some embodiments, query string generator 510 generates exclusionterms. Generally, some schema features can imply negative conditions.For example, particular music release eras (e.g., musical features inthe release:era schema category) can imply a rejection of particulargenres. Accordingly, one or more genre exclusions can be defined, forexample, to prevent a search for new or unreleased music (e.g.,release:era: 2010_and_later) from matching songs in the “adult classics”genre. Exclusions such as genre exclusions can be stored, for example,in a dictionary (e.g., dictionary 210). In some embodiments, exclusionscan be personalized for a given user. Generally, it is possible forusers to imply different constraints using the same language. Forexample, when different users refer to pop, the users might actuallymean to suggest different sub-genres of pop. Accordingly, someembodiments can include personalized dictionaries for a particular useror group of users. In this manner, query string generator 510 can accessdefined exclusions to generate exclusion terms applicable to features tobe searched (e.g., features appearing in the feature map).

Query string generator 510 combines the generated query terms for agiven valence level to generate a combined query term for that valencelevel. In embodiments that include exclusions, query string generator510 includes generated exclusion terms in the combined query term. Thisprocess can be repeated for each valence level appearing in the featuremap, and the resulting terms for each valence level combined to form thequery string. In some embodiments, query string generator 510 identifiesa target catalog from terms metadata, generates a corresponding catalogconstraint, and includes catalog constraints in the query string. Inthis manner, query string generator 510 can generate a query string froma set of features to be searched. Below is an example query string thatcan be generated based on a music description and categorization schema,including boost multipliers:

+(cSF:“rhythmic”{circumflex over ( )}14.0 cSF:“danceable”{circumflexover ( )}14.0 cSF:“happy”{circumflex over ( )}14.0cSF:“euphoric”{circumflex over ( )}14.0){circumflex over ( )}10.0+(cSF:“vocals_with_music”{circumflex over ( )}4.0cSF:“instrumental”{circumflex over ( )}4.0){circumflex over ( )}10.0(cSF:“organic_acoustic_or_electric”{circumflex over ( )}10.0cSF:“acoustic”{circumflex over ( )}10.0){circumflex over ( )}10.0+(cSF:“midtempo”{circumflex over ( )}4.0 cSF:“uptempo”{circumflex over( )}4.0){circumflex over ( )}10.0 +(gF:“Folk”{circumflex over ( )}1.2gF:“folk”{circumflex over ( )}1.2){circumflex over ( )}10.0 −gF:“folkmetal”{circumflex over ( )}1.2 −gF:“folk punk”{circumflex over ( )}1.2−gF:“folk metal”{circumflex over ( )}1.2 −gF:“folk punk”{circumflex over( )}1.2 (aF:“driving rhythm”{circumflex over ( )}1.0aF:“Organic”{circumflex over ( )}1.0 aF:“Folky”{circumflex over ( )}1.0aF:“rhythmic”{circumflex over ( )}1.0 aF:“organic”{circumflex over( )}1.0 aF:“folky”{circumflex over ( )}1.0 aF:“nice”{circumflex over( )}1.0 aF:“scarce”{circumflex over ( )}1.0 aF:“vocals”{circumflex over( )}1.0 aF:“instrumentals”{circumflex over ( )}1.0 aF:“gooddrive”{circumflex over ( )}1.0 aF:“positive”{circumflex over( )}1.0){circumflex over ( )}10.0 +(cSF:“danceable”{circumflex over( )}14.0 cSF:“dynamic”{circumflex over ( )}14.0cSF:“reflective”{circumflex over ( )}14.0 cSF:“rhythmic”{circumflex over( )}14.0){circumflex over ( )}5.0 +(cSF:“clean”{circumflex over ( )}4.0cSF:“vocals_with_music”{circumflex over ( )}4.0 cSF:“studio”{circumflexover ( )}4.0 cSF:“instrumental”{circumflex over ( )}4.0){circumflex over( )}5.0 (cSF:“organic_acoustic_or_electric”{circumflex over ( )}10.0cSF:“organic_electric_or_electronic”{circumflex over ( )}10.0cSF:“electric_or_electronic”{circumflex over ( )}10.0){circumflex over( )}5.0 (cSF:“calm_intro_or_fade_in”{circumflex over ( )}8.0cSF:“normal_outtro”{circumflex over ( )}8.0cSF:“multiple_crescendos”{circumflex over ( )}8.0cSF:“calm_outtro_or_fade_out”{circumflex over ( )}8.0){circumflex over( )}5.0 +(cSF:“midtempo”{circumflex over ( )}4.0cSF:“uptempo”{circumflex over ( )}4.0){circumflex over ( )}5.0(cSF:“known”{circumflex over ( )}2.0 cSF:“popular”{circumflex over( )}2.0){circumflex over ( )}5.0 +(cSF:“contemporary”{circumflex over( )}2.0 cSF:“2010_and_later”{circumflex over ( )}2.0cSF:“00s”{circumflex over ( )}2.0){circumflex over ( )}5.0+(gF:“acoustic pop”{circumflex over ( )}1.2 gF:“chamber pop”{circumflexover ( )}1.2 gF:“folk-pop”{circumflex over ( )}1.2 gF:“indieanthem-folk”{circumflex over ( )}1.2 gF:“indie folk”{circumflex over( )}1.2 gF:“new americana”{circumflex over ( )}1.2 gF:“stomp andholler”{circumflex over ( )}1.2 gF:“indie pop”{circumflex over ( )}1.2gF:“folk”{circumflex over ( )}1.2){circumflex over ( )}5.0 −gF:“folkmetal”{circumflex over ( )}1.2 − gF:“folk punk”{circumflex over ( )}1.2−gF:“adult standards”{circumflex over ( )}1.2 −gF:“classic *”{circumflexover ( )}1.2 (aF:“matthew and the atlas”{circumflex over ( )}1.0 aF:“anartist on spotify”{circumflex over ( )}1.0 aF:“jacksonvillebeach”{circumflex over ( )}1.0 aF:“florida-based radical face isprimarily ben cooper”{circumflex over ( )}1.0 aF:“who is also one-halfof similar quiet-is-the-new-loud duo electric president.”{circumflexover ( )}1.0){circumflex over ( )}5.0 (raF:“matthew and theatlas”{circumflex over ( )}1.0 raF:“benjamin francisleftwich”{circumflex over ( )}1.0 raF:“marcus foster”{circumflex over( )}1.0 raF:“johnny flynn”{circumflex over ( )}1.0 raF:“dry theriver”{circumflex over ( )}1.0 raF:“roo panes”{circumflex over ( )}1.0raF:“bear's den”{circumflex over ( )}1.0 raF:“the paperkites”{circumflex over ( )}1.0 raF:“luke sital-singh”{circumflex over( )}1.0 raF:“stu larsen”{circumflex over ( )}1.0 raF:“dustintebbutt”{circumflex over ( )}1.0 raF:“horse feathers”{circumflex over( )}1.0 raF:“james vincent mcmorrow”{circumflex over ( )}1.0 raF:“littlemay”{circumflex over ( )}1.0 raF:“beta radio”{circumflex over ( )}1.0raF:“s. carey”{circumflex over ( )}1.0 raF:“novo amor”{circumflex over( )}1.0 raF:“donovan woods”{circumflex over ( )}1.0 raF:“gregory alanisakov”{circumflex over ( )}1.0 raF:“i am oak”{circumflex over ( )}1.0raF:“sons of the east”{circumflex over ( )}1.0 raF:“radicalface”{circumflex over ( )}1.0 raF:“electric president”{circumflex over( )}1.0 raF:“sea wolf”{circumflex over ( )}1.0 raF:“blindpilot”{circumflex over ( )}1.0 raF:“noah and the whale”{circumflex over( )}1.0 raF:“the tallest man on earth”{circumflex over ( )}1.0raF:“rogue wave”{circumflex over ( )}1.0 raF:“alexi murdoch”{circumflexover ( )}1.0 raF:“milo greene”{circumflex over ( )}1.0 raF:“the ohhellos”{circumflex over ( )}1.0 raF:“freelance whales”{circumflex over( )}1.0 raF:“the middle east”{circumflex over ( )}1.0 raF:“volcanochoir”{circumflex over ( )}1.0 raF:“william fitzsimmons”{circumflex over( )}1.0){circumflex over ( )}5.0 − (cSF:“kitschy”{circumflex over( )}14.0){circumflex over ( )}5.0 −(aF:“kitschy”{circumflex over( )}1.0){circumflex over ( )}5.0

Below is an example catalog document that can be generated for a searchresult, including a ranking score. This example document is merely meantas an example. Other variations of documents, data structures and/orcorresponding fields may be implemented within the present disclosure.

{  “id”: “16e6ced0-eff1-4349-bda2-f427aa7597e0::spotify:track:07ZkpHysDtnBHQKbEOBZ2L”,  “catalogId”:“16e6ced0-eff1-4349-bda2-f427aa7597e0”,  “songName”: “Rivers”, “searchableSongName”: “Rivers”, “songPreviewUrl”:“http://open.spotify.com/embed?uri=spotify:track:07ZkpHysDtnBHQKbEOBZ2L”, “songDuration”: 236853,  “artistName”: “The Tallest Man On Earth”, “artistUrl”:“http://open.spotify.com/embed?uri=spotify:artist:2BpAc5eK7Rz5GAwSp9UYXa”, “albumName”: “Rivers”,  “searchableAlbumName”: “Rivers”,  “albumUrl”:“”,  “releaseDate”: “Wed Aug 17 00:00:00 PDT 2016”, “releaseDatePrecision”: “”,  “cSF”: “rhythmic, mellow, reflective,clean, vocals_with_music, studio, acoustic, calm_intro_or_fade_in,normal_outtro, multiple_crescendos, midtempo, popular, new,2010_and_later”,  “gF”: “chamber pop, folk-pop, freak folk, indie folk,indie pop, indie rock, neo mellow, new americana, singer-songwriter,stomp and holler”,  “aF”: “Playing spare but tuneful indie folkenlivened by his sometimes craggy, always passionate vocals and poeticlyrics, the Tallest Man on Earth is the stage name of Swedish singer andsongwriter Kristian Matsson.”,  “iF”: “”,  “raF”: “The Tallest Man OnEarth, Horse Feathers, Blind Pilot, Johnny Flynn, Iron & Wine,Phosphorescent, M. Ward, Deer Tick, Fleet Foxes, Volcano Choir,Langhorne Slim, Gregory Alan Isakov, Alexi Murdoch, Damien Jurado, NoahAnd The Whale, J. Tillman, Edward Sharpe & The Magnetic Zeros, FruitBats, Andrew Bird, Radical Face, Joe Pug”,  “lyrics”: “”,  “score”:3.4226194637764458 }

Generally, search execution component 520 executes a search using thegenerated query string on a defined index (e.g., catalog index 220).Various full text search engines can be implemented using customschemas, as would be understood by a person of ordinary skill in theart. In order to facilitate ranking and review, for each search result,search execution component 520 can create a record (e.g., a catalogdocument), and add the record to a primary search result set and/or ahashmap (e.g., for statistical analysis). The search process can bestopped once it produces a defined limit for the number of searchresults. In some embodiments, search execution component 520 can executeone or more alternate (e.g., wildcard) queries using relaxed searchcriteria (e.g., relaxed boost multipliers). For each wildcard searchresult, if the result does not appear in the primary search result setand/or hashmap, search execution component 520 can create a record(e.g., a catalog document), and add the record to a wildcard searchresult set and/or a wildcard hashmap (e.g., for statistical analysis).The wildcard search process can be stopped once it produces a definedlimit for the number of wildcard search results.

Ranking and statistics component 530 performs an analysis on searchresults to facilitate efficient user review. In the embodiment depictedby FIG. 5, ranking and statistics component 530 includes baselinegenerator 532, ranking generator 534 and statistics generator 536.Baseline generator 532 can generate a baseline score that can be used torank search results. For example, baseline generator 532 can access asystem document that includes a representation of all schema featuresappearing in a catalog, for example, an all-match document including arepresentation of all musical features appearing in the catalog index.The all-match document is a match for every possible query, excludingnegative terms and transforming some terms e.g. related artists, andpartitioned keywords. Accordingly, baseline generator 532 preferablyremoves negative query terms from the query string and transforms someruntime terms to the query string before applying the resulting query tothe all-match document. In this manner, baseline generator 532 executesa search on the all-match document using the generated query string, ora derivation thereof, to produce a baseline maximum score (e.g., an“all-match score”). For example, boost multipliers for each matchedquery term in the query string can be combined (e.g., added) to generatethe all-match score.

The all-match score can be adjusted (e.g., by one or more tunablenumeric factors) based on overmatching potential and/or querycomplexity. Overmatching of a query string to the all-match document canoccur based on an exclusion of negative terms (broadening the surfacearea of the baseline request). Moreover, certain keyword combinationsmay be unlikely or even not allowed (e.g., keywords corresponding tomusical features from a partitioned schema category). Overmatching (orundermatching) can increase with an increasing query complexity (e.g.,the number of query terms). Accordingly, the all-match score may beadjusted by one or more factors.

Ranking generator 534 and/or statistics generator 536 determine scoresfor returned search results and matched keywords. For example, rankinggenerator 534 can identify a ranking score for each search result (e.g.,each catalog document in a primary/wildcard results set) based on boostmultipliers for matched query terms for that search result. For example,boost multipliers for each matched query term in the query string can becombined (e.g., added) for a given search result to generate the rankingscore for that search result. Statistics generator 536 can convertranking scores to normalized scores (e.g., on a fixed scale such as fivestars) using the all-match document as the baseline for a strong match.Additionally and/or alternatively, statistics generator 536 can computestatistics on collections of scored search results (e.g., count, meanscore, and standard deviation). For example, for each schema facet(i.e., category, sub-category, category instance) that includes a searchresult, statistics generator 536 can determine a count of returnedsearch results for the facet. For example, for each matched keyword(musical feature), statistics generator 536 can determine a count of thenumber of songs (e.g., catalog documents) that matched the keyword. Insome embodiments, statistics generator 536 initializes the statisticsusing a baseline (e.g., the all-match score) to influence standarddeviation computations. In this manner, matchmaking component 500generates ranks and computes statistics for search results.

Having narrowed the result space (e.g., from potentially millions tohundreds of the most relevant songs), tools are provided for efficientlysifting through the search results, and for selecting and sharingcandidate songs. Turning now to FIG. 6, FIG. 6 illustrates exemplaryenvironment 600 with user device 610 and application 620 (which maycorrespond to app 115 in FIG. 1). The application may be a stand-aloneapplication, a mobile application, a web application, or the like.Generally, a user inputs a natural language description of music into anapplication (e.g., application 620) on a user device (e.g., user device610), and the application provides a user interface that provides theuser with one or more visualizations of search results and/or enablesthe user to select candidate songs, share selected songs, and the like.

In the embodiment depicted in FIG. 6, application 620 includestranslation visualization component 630, results visualization component640 and candidates component 650. Likewise, in the embodiment depictedin FIG. 6, results visualization component 640 includes most relevantresults component 642, word cloud component 644, deep cuts component 646and wildcard results component 648. Generally, translation visualizationcomponent 630 provides a visualization of a translated search (e.g.,schema features, genres, related artists, etc., generated from a rawinput). Most relevant results component 642 provides a visualization ofthe most relevant (e.g., highest ranked) search results from a primarysearch result set. Deep cuts component 646 provides a visualization ofadditional search results from the primary search result set. Word cloudcomponent 644 generates and provides word cloud representations ofmatched keywords (i.e., schema features) and/or schema categories fromof a translated search (e.g., to filter results based on generatedschema features and/or schema categories.). Wildcard results component648 provides a visualization of search results from a wildcard searchset. Candidates component 650 provides a visualization of search resultsthat have been selected as candidate songs (e.g., tagged by a user). Ofcourse, these visualizations are merely exemplary, and variations ofvisualizations can be implemented by a person of ordinary skill in theart.

Turning now to FIGS. 7-14, FIGS. 7-14 depict exemplary user interfacesof an application such as application 620 of FIG. 6. FIG. 7 illustratesexemplary user interface 700 for receiving a natural language inputcomprising a description of music. User interface 700 includes inputfield 710 and analyze search button 720. Generally, a user enters anatural language description of music such as music description 715 andpresses analyze search button 720 to perform a search for music thatmatches the description.

FIGS. 8-14 depict exemplary user interfaces that include some commonelements. For example, the depicted user interfaces include navigationalbuttons that allow a user to navigate between screens withvisualizations of the translated search, search results and candidatesongs. For example, search buttons 820, 920, 1020, 1120, 1320 and 1420allow a user to navigate to user interface 800 depicting a visualizationof the translated search. Marketplace buttons 822, 922, 1022, 1122,1222, 1322 and 1422 allow a user to navigate to a user interfacedepicting a visualization of search results (e.g., user interfaces 900,1000, 1100, 1200 or 1300). Candidates buttons 824, 924, 1024, 1124,1224, 1324 and 1424 allow a user to navigate to user interface 1400depicting a visualization of songs selected by a user as candidatesongs. When a particular user interface is selected, a correspondingbutton may be highlighted, shaded or otherwise indicate the selecteduser interface. For example, search button 820 is highlighted in FIG. 8,indicating user interface 800 with a visualization of a translatedsearch is being presented.

The user interfaces depicted in FIGS. 8-14 also include a representationof the natural language being analyzed (in these examples, musicdescription 715 of FIG. 7). For example, fields 810, 910, 1010, 1110,1210, 1310 and 1410 are populated with a representation of musicdescription 715, including a selection of extracted features. Thefeatures displayed in these fields can be selected to provide adistilled version of the input description, for example, by presentingschema features generated from the description. Edit search buttons 815,915, 1015, 1115, 1215, 1315 and 1415 can be selected by a user to editthe description being analyzed.

FIG. 8 illustrates exemplary user interface 800 depicting avisualization of a translated search. User interface 800 can begenerated by translation visualization component 630 of FIG. 6.Generally, user interface 800 can include visualization elements forbuild terms, a visual representation of the translated search and/orgenerated (i.e., extracted) schema features (e.g., core features,genres, related artists, etc.). For example, user interface 800 includesterms header 830 for build terms, including title 832 (which can beimplied from the first line of the description of music being analyzed),deadline 834 and budget 836. Generally, a visualization of the analyzeddescription of music can be provided to indicate aspects of thetranslation. As such, user interface 800 includes search header 840 foranalyzed search 845. In a visualization of the analyzed description,phrases from the description of music can be highlighted and/orformatted to indicate aspects of the translation (e.g., by highlightingphrases that were translated into schema features, striking out phasesthat were interpreted as negative sentiments, bolding phrases that wereinterpreted as a song reference, etc.). In this sense, the visualizationof the analyzed description of music can provide a feedback mechanismfor a user to understand and interpret the translation, and makechanges, if necessary (e.g., via edit search button 815).

In some embodiments, a user interface can include visualizations ofgenerated schema features. For example, user interface 800 includesextracted schema features header 850 and extracted schema features 855,extracted genres header 860 and extracted genres 865, and extractedrelated artists header 870 and extracted related artists 875.Visualizations of generated schema features can be features fromselected schema categories (e.g., designated core schema categories suchas extracted schema features 855). In some embodiments, one or moreschema categories (e.g., genre, related artists, etc.) can be separatedout and presented separately. In this manner, extracted genres 865 andextracted related artists 875 provide a visualization of features fromselected schema categories. The selection and arrangement of schemacategories from which to present generated features facilitatesmodifications to the translation. For example, a user can select editbuttons 857, 867 or 877 to make changes to extracted schema features855, extracted genres 865 or extracted related artists 875,respectively.

In some embodiments, formatting of visual elements in various sectionsof the user interface can be matched to indicate an association. Forexample, the formatting of visual elements for a phrase of analyzedtext, a corresponding schema feature, a corresponding schema categoryand/or one or more corresponding visual elements (e.g., a header,legend, text color, etc.) can be matched. In the example depicted inFIG. 8, highlighting of phrases in field 810 and analyzed search 845matches the formatting (e.g., color, shading, etc.) of the header of acorresponding schema category below. More specifically, the formattingof extracted schema features header 850 (e.g., thatched pattern) matchesthe formatting of highlighting for phrases in analyzed search 845 andfeatures in field 810 corresponding to the features in extracted schemafeatures 855 (e.g., also thatched). Likewise, the formatting ofextracted genre header 860 (e.g., dotted pattern) matches the formattingof highlighting for phrases in analyzed search 845 and features in field810 corresponding to features in extracted genre 865 (e.g., alsodotted). Similarly, the formatting of extracted related artist header870 (e.g., vertical line pattern) matches the formatting of highlightingfor phrases in analyzed search 845 and features in field 810corresponding to features in extracted related artists 865 (e.g., alsovertical lines). This format matching helps a user to understand andinterpret the translation, and make changes, if necessary (e.g., viaedit buttons 857, 867 or 877).

As such, user interface 800 provides a mechanism for a user to makechanges, or tune, a translation. This tuning can be used to improve thetranslation. Generally, language translation using software (e.g.,machine translation) can be improved using artificial intelligence (AI)techniques combined with access to Internet scale training data.Accordingly, in some embodiments, machine translation can beaccomplished in two stages. The first stage uses natural languageprocessing to translate a natural language description, as describedabove. A user interface such as user interface 800 can be provided toallow a user to validate, augment and/or correct the translated search.User decisions to validate, augment and/or correct can be archived andanalyzed to generate translation training data. In this manner, a secondstage of machine translation can use trained AI models to assist thefirst stage components, and over time, the combined techniques form aconstantly improving translation system.

Turning now to FIGS. 9-13, FIGS. 9-13 depict exemplary user interfacesthat include some common elements. For example, the depicted userinterfaces include navigational buttons that allow a user to navigatebetween screens with a visualization of the most relevant results from aprimary search set, word cloud visualizations that can filter searchresults by schema category and/or keyword, additional results from aprimary search set, and search results from a wildcard search set. Forexample, Most Relevant buttons 926, 1026, 1126, 1226 and 1326 allow auser to navigate to user interface 900 depicting a visualization of themost relevant search results from a primary search set. Deep Cutsbuttons 930, 1030, 1130, 1230 and 1330 and Wildcards buttons 932, 1032,1132, 1232 and 1332 allow a user to navigate to a user interface similarto user interface 900, but depicting a visualization of additionalresults from a primary search set, and a visualization of search resultsfrom a wildcard search set, respectively. Word Clouds buttons 928, 1028,1128, 1228 and 1328 allow a user to navigate to a user interfacedepicting one or more word clouds that can filter search results byschema category and/or keyword (e.g., user interfaces 1000, 1100, 1200or 1300). When a particular user interface is selected, a correspondingbutton may be highlighted, shaded or otherwise indicate the selecteduser interface. For example, Most Relevant button 926 is highlighted inFIG. 9, indicating user interface 900 with a visualization of the mostrelevant search results is being presented.

FIG. 9 illustrates exemplary user interface 900 depicting avisualization of search results. User interface 900 can be generated byresults visualization component 640 of FIG. 6 (e.g., most relevantresult component 642). Generally, search results can be presented invarious ways, such as by displaying lists, tiles or otherrepresentations of search results. Additionally and/or alternatively,search results can be presented using text (e.g., song title and artist,song description, etc.), one or more images (e.g., album art), embeddedlinks (e.g., links to an audio or video recording of the song), embeddedaudio or video (e.g., an audio or video recording of the song), searchratings, matched schema features, statistics about matched schemafeatures, source catalog, candidate song tags and the like, and anycombination thereof.

For example, in the embodiment illustrated by FIG. 9, user interface 900includes search results 940 a, 940 b and 940 c. Search result 940 a, forexample, includes a corresponding playback button and progress bar 942,ranking score 944, matched schema features 956 (and correspondingmatched features count 946), matched genres 958 (and correspondingmatched genre count 948), matched related artists 960 (and correspondingmatched related artists count 950), source catalog indicator 952 andcandidate song tag 954 a. If a user is interested in selecting a searchresult as a candidate song for further review, a candidate song tag canbe selected for the search result. For example, in the embodimentillustrated by FIG. 9, candidate song tags 954 a and 954 c have beenchecked, while candidate song tag 954 b has not. It should be understoodthat user interface 900 is merely exemplary. Variations on thearrangement and presentation of search results can be implemented withinthe present disclosure. Similar user interfaces can be provided topresent visualizations of most relevant search results from a primarysearch set (e.g., the search results with the highest ranking score),additional results from a primary search set, search results from awildcard search set, filtered search results (e.g., in connection with akeyword from a word cloud), selected candidate songs, and the like.

Turning now to FIGS. 10-13, FIGS. 10-13 depict exemplary user interfaces1000, 1100, 1200 and 1300 that present word clouds that can be used tofilter search results by schema category and/or keyword. User interfaces1000, 1100, 1200 and 1300 can be generated by word cloud component 644of FIG. 6. Generally, word clouds can be provided that correspond toselected schema categories and/or matched keyword. For example, a wordcloud can be provided with phrases corresponding to matched keywords ina selected schema category, where the size of each matched keyword inthe word cloud corresponds to the count of search results that matchedthat keyword. In this manner, a user can select a keyword in the wordcloud to filter the search results to display results that matched theselected keyword.

Word clouds can be provided for selected schema categories. The selectedschema categories can correspond with the categories of generated schemafeatures presented in user interface 800 depicting a visualization of atranslated search. For example, word clouds can be provided for matchedcore schema features, matched genres and matched related artists, toname a few possibilities. For example, word cloud 1040 of FIG. 10depicts a word cloud with matched schema features corresponding toextracted schema features 855 of FIG. 8. Word clouds 1140 of FIGS. 11and 1340 of FIG. 13 depict word clouds with matched genres correspondingto extracted genres 865 of FIG. 8. Word cloud 1240 of FIG. 12 depicts aword cloud with matched related artists corresponding to extractedrelated artists 875 of FIG. 8. Generally, a user interface can includenavigational tools that allow a user to navigate between word clouds(e.g., left and right navigational arrows, links, etc.).

FIG. 13 illustrates a user interface with a matched genre word cloud andsearch results filtered by a selected genre. User interface 1300includes word cloud 1340 depicting genre keywords that matched searchresults. One of the matched keywords (keyword 1345, i.e., noise pop) hasbeen selected (as indicated by the highlighting of keyword 1345). Inthis embodiment, selecting a keyword from word cloud 1340 generates avisualization of search results filtered by the keyword (i.e., searchresults that matched the selected keyword). Accordingly, search results1350 a and 1350 b are displayed on user interface 1300.

Turning now to FIG. 14, FIG. 14 illustrates exemplary user interface1400 depicting a visualization of candidate songs. User interface 1400can be generated by candidates component 650 of FIG. 6. Generally, auser can select songs for further review (e.g., using candidate songtags on any of the previous user interfaces). User interface 1400generally identifies songs that have been tagged as candidates andpresents the candidate songs for review. For example, user interface1400 includes candidate songs 1440 a, 1440 b, 1440 c and 144 d that weretagged as candidate songs. Songs can be presented with a removal button(e.g., remove button 1442) that can be selected to remove a song as acandidate (e.g., untag the song). Additionally and/or alternatively,songs can be presented with a notes field (e.g., notes field 1444) foruser notes. In some embodiments, a user interface provides an indicationof the number of candidate songs that have been tagged. For example,candidates buttons 1424 includes the number five, indicating that fivesongs have been tagged as candidates (although only four are displayedin the snapshot of user interface 1400). In some embodiments, theselection of candidate songs may be shared with other users.

Exemplary Flow Diagrams

With reference to FIGS. 15-19, flow diagrams are provided illustratingmethods for generating search results from a natural language searchrequest for music. Each block of methods 1500, 1600, 1700, 1800, 1900and any other methods described herein comprises a computing processperformed using any combination of hardware, firmware, and/or software.For instance, various functions can be carried out by a processorexecuting instructions stored in memory. The methods can also beembodied as computer-usable instructions stored on computer storagemedia. The methods can be provided by a standalone application, aservice or hosted service (standalone or in combination with anotherhosted service), or a plug-in to another product, to name a few.

Turning now to FIG. 15, a flow diagram is provided that illustrates amethod 1500 for ingesting music. Initially at block 1510, a musiccatalog is accessed, including structured and/or unstructured metadata.The access can be facilitated using a catalog plugin. At block 1520,format conversion is performed to convert the structured metadata intocorresponding musical features of a music description and categorizationschema. At block 1530, language translation is performed to convert theunstructured metadata to corresponding musical features of the schema.At block 1540, a music catalog index is populated based on the formatconversion and/or the language translation. At block 1545, a file isgenerated that includes all musical features appearing in the musiccatalog index. For example, the file can be an all-match document, asdescribed herein. At block 1550, a reference database is populated basedon the format conversion and/or the language translation.

Turning now to FIG. 16, a flow diagram is provided that illustrates amethod 1600 for generating an annotated input. Initially at block 1610,a natural language description of music is accessed. At block 1620, thenatural language input is segmented into spans, and each span isassigned a valence categorization. At block 1630, references to musicare identified from the description of music, corresponding referencespans are created, and corresponding valence categorizations areupdated. At block 1640, an annotated input is generated comprising thespans and the valence categorizations.

Turning now to FIG. 17, a flow diagram is provided that illustrates amethod 1700 for generating schema features from an annotated input.Initially at block 1710, an annotated input is accessed. The annotatedinput comprises spans and corresponding valence categorizations. Atblock 1720, the annotated input is parsed into constituent spans.Generally, the spans can identify a sentence or a reference from theannotated input. At block 1730, musical features are accessed forresolved references. For example, the musical features can be looked upfrom a reference database. At block 1740, musical features areidentified for spans that identify a sentence. For example, mentions ofmusical characteristics are extracted from the spans, and the mentionsare translated to corresponding musical features. Artist mentions areextracted from the spans, and corresponding musical features areaccessed (e.g., from the reference database). At block 1750, the musicalfeatures are stored with corresponding valence categorizations in a datastructure. At block 1760, one or more data structures are generated thatassociate the valence categorizations of the annotated input withcorresponding identified musical features. At block 1770, fuzzing isapplied to the musical features in the one or more data structures.

Turning now to FIG. 18, a flow diagram is provided that illustrates amethod 1800 for executing a search based on a set of schema features.Initially at block 1810, one or more data structures are accessed thatassociate musical features with corresponding valence categorizations.The valence categorizations are selected from a set of valence levels.At block 1820, a query term profile is accessed. The query term profilecomprises a query term generator for musical feature categories and forthe valence levels. At block 1830, query terms are generated using thequery term generators. At block 1840, exclusion terms are generated. Atblock 1850, generated query terms (including exclusion terms) arecombined for each valence level. At block 1860, generated query termsfor each valence level are combined to form a query string. At block1870, a search is executed on a music catalog index using the querystring.

Turning now to FIG. 19, a flow diagram is provided that illustrates amethod 1900 for ranking search results. Initially at block 1910, anall-match file is generated. The all-match file includes all musicalfeatures appearing in a music catalog index. At block 1920, a querystring is accessed and a modified query string is generated thatexcludes negative terms of the query string and transforms some terms ofthe query string. At block 1930, a search is executed on the all-matchfile using the modified search string to generate an all-match score. Atblock 1940, the all-match score is adjusted to compensate forovermatching and/or query complexity. At block 1950, a search isexecuted on the music catalog index using the query string to generatesearch results and matched keywords of the search string. At block 1960,ranking scores are identified for the search results based on boostmultipliers for matched query terms for a given search result. At block1970, counts of search results are determined that matched the matchedkeywords. At block 1980, the ranking scores for the search results areconverted to normalized ranking scores using the all-match file.

Exemplary Operating Environment

Having described an overview of embodiments of the present invention, anexemplary operating environment in which embodiments of the presentinvention may be implemented is described below in order to provide ageneral context for various aspects of the present invention. Referringnow to FIG. 20 in particular, an exemplary operating environment forimplementing embodiments of the present invention is shown anddesignated generally as computing device 2000. Computing device 2000 isbut one example of a suitable computing environment and is not intendedto suggest any limitation as to the scope of use or functionality of theinvention. Neither should the computing device 2000 be interpreted ashaving any dependency or requirement relating to any one or combinationof components illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a cellular telephone, personal data assistant orother handheld device. Generally, program modules (including routines,programs, objects, components, data structures, etc.), refer to codethat perform particular tasks or implement particular abstract datatypes. The invention may be practiced in a variety of systemconfigurations, including hand-held devices, consumer electronics,general-purpose computers, more specialty computing devices, etc. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote-processing devices that are linkedthrough a communications network.

With reference to FIG. 20, computing device 2000 includes bus 2010 thatdirectly or indirectly couples the following devices: memory 2012, oneor more processors 2014, one or more presentation components 2016,input/output ports 2018, input/output components 2020, and illustrativepower supply 2022. Bus 2010 represents what may be one or more buses(such as an address bus, data bus, or combination thereof). The variousblocks of FIG. 20 are shown with lines for the sake of conceptualclarity, and other arrangements of the described components and/orcomponent functionality are also contemplated. For example, one mayconsider a presentation component such as a display device to be an I/Ocomponent. Also, processors have memory. We recognize that such is thenature of the art, and reiterate that the diagram of FIG. 20 is merelyillustrative of an exemplary computing device that can be used inconnection with one or more embodiments of the present invention.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “hand-held device,” etc., as all are contemplatedwithin the scope of FIG. 20 and reference to “computing device.”

Computing device 2000 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 2000 and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable media may comprise computerstorage media and communication media. Computer storage media includevolatile and nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computing device 2000. Computer storagemedia excludes signals per se. Communication media typically embodiescomputer-readable instructions, data structures, program modules orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media. Combinations of any of the above should also be includedwithin the scope of computer-readable media.

Memory 2012 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Exemplary hardware devices include solid-statememory, hard drives, optical-disc drives, etc. Computing device 2000includes one or more processors that read data from various entitiessuch as memory 2012 or I/O components 2020. Presentation component(s)2016 present data indications to a user or other device. Exemplarypresentation components include a display device, speaker, printingcomponent, vibrating component, etc.

I/O ports 2018 allow computing device 2000 to be logically coupled toother devices including I/O components 2020, some of which may be builtin. Illustrative components include a microphone, joystick, game pad,satellite dish, scanner, printer, wireless device, etc. The I/Ocomponents 2020 may provide a natural user interface (NUI) thatprocesses air gestures, voice, or other physiological inputs generatedby a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described in more detail below) associated with adisplay of the computing device 2000. The computing device 2000 may beequipped with depth cameras, such as stereoscopic camera systems,infrared camera systems, RGB camera systems, touchscreen technology, andcombinations of these, for gesture detection and recognition.Additionally, the computing device 2000 may be equipped withaccelerometers or gyroscopes that enable detection of motion. The outputof the accelerometers or gyroscopes may be provided to the display ofthe computing device 2000 to render immersive augmented reality orvirtual reality.

Embodiments described herein support the generation of search resultsfrom a natural language search request for music. The componentsdescribed herein refer to integrated components for natural languagesearching. The integrated components refer to the hardware architectureand software framework that support natural language searchfunctionality. The hardware architecture refers to physical componentsand interrelationships thereof and the software framework refers tosoftware providing functionality that can be implemented with hardwareembodied on a device.

The end-to-end software-based system can operate within systemcomponents to operate computer hardware to provide system functionality.At a low level, hardware processors execute instructions selected from amachine language (also referred to as machine code or native)instruction set for a given processor. The processor recognizes thenative instructions and performs corresponding low level functionsrelating, for example, to logic, control and memory operations. Lowlevel software written in machine code can provide more complexfunctionality to higher levels of software. As used herein,computer-executable instructions include any software, including lowlevel software written in machine code, higher level software such asapplication software and any combination thereof. In this regard, thesystem components can manage resources and provide services for thenatural language search functionality. Any other variations andcombinations thereof are contemplated with embodiments of the presentdisclosure.

By way of example, a natural language search system can include an APIlibrary that includes specifications for routines, data structures,object classes, and variables may support the interaction between thehardware architecture of the device and the software framework of thenatural language search system. These APIs include configurationspecifications for the natural language search system such that thedifferent components therein can communicate with each other in thenatural language search system, as described herein.

Having identified various components in the present disclosure, itshould be understood that any number components and arrangements may beemployed to achieve the desired functionality within the scope of thepresent disclosure. For example, the components in the embodimentsdepicted in the figures are shown with lines for the sake of conceptualclarity. Other arrangements of these and other components may also beimplemented. For example, although some components are depicted assingle components, many of the elements described herein may beimplemented as discrete or distributed components or in conjunction withother components, and in any suitable combination and location. Someelements may be omitted altogether. Moreover, various functionsdescribed herein as being performed by one or more entities may becarried out by hardware, firmware, and/or software, as described below.For instance, various functions may be carried out by a processorexecuting instructions stored in memory. As such, other arrangements andelements (e.g., machines, interfaces, functions, orders, and groupingsof functions, etc.) can be used in addition to or instead of thoseshown.

Moreover, although embodiments described above relate to a musicdescription and categorization schema, any description andcategorization schema can be implemented based on a desired domain(e.g., music, audio books, podcasts, videos, events, ticketing, realestate, etc.). Accordingly, some or all of the components and/orfunctions described above can be adapted to a desired domain, as wouldbe understood by a person of ordinary skill in the art.

The subject matter of embodiments of the invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent elements of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

The present invention has been described in relation to particularembodiments, which are intended in all respects to be illustrativerather than restrictive. Alternative embodiments will become apparent tothose of ordinary skill in the art to which the present inventionpertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

What is claimed is:
 1. A computerized method comprising: causing a userinterface to present a word cloud of features that were extracted from anatural language description and that matched search results of thenatural language description; generating, based on an input identifyinga selected feature of the features from the word cloud, filtered searchresults that match the selected feature; and causing the user interfaceto present a representation of the filtered search results that matchthe selected feature from the word cloud.
 2. The computerized method ofclaim 1, wherein the features are musical features of a musicdescription and categorization schema, the natural language descriptionis of music, and the search results are songs.
 3. The computerizedmethod of claim 1, wherein the word cloud represents each feature of thefeatures as a corresponding word or phrase having a size thatcorresponds at least in part to a count of the search results thatmatched the feature.
 4. The computerized method of claim 1, furthercomprising extracting the features by extracting chunks of text from thenatural language description and translating the chunks of text intocorresponding features of a description and categorization schema. 5.The computerized method of claim 1, wherein the features represented inthe word cloud are instances of core schema categories that wereextracted from the natural language description and that matched thesearch results.
 6. The computerized method of claim 1, wherein thefeatures represented in the word cloud are genres that were extractedfrom the natural language description and that matched the searchresults.
 7. The computerized method of claim 1, wherein the featuresrepresented in the word cloud are related artists that were extractedfrom the natural language description and that matched the searchresults.
 8. One or more computer storage media storing computer-useableinstructions that, when executed by one or more computing devices, causethe one or more computing devices to perform operations comprising:causing a user interface to present a word cloud of features that wereextracted from a natural language description and that matched searchresults of the natural language description; generating, based on aninput identifying a selected feature of the features from the wordcloud, filtered search results that match the selected feature; andcausing the user interface to present a representation of the filteredsearch results that match the selected feature from the word cloud. 9.The one or more computer storage media of claim 8, wherein the featuresare musical features of a music description and categorization schema,the natural language description is of music, and the search results aresongs.
 10. The one or more computer storage media of claim 8, whereinthe word cloud represents each feature of the features as acorresponding word or phrase having a size that corresponds at least inpart to a count of the search results that matched the feature.
 11. Theone or more computer storage media of claim 8, the operations furthercomprising extracting the features by extracting chunks of text from thenatural language description and translating the chunks of text intocorresponding features of a description and categorization schema. 12.The one or more computer storage media of claim 8, wherein the featuresrepresented in the word cloud are instances of core schema categoriesthat were extracted from the natural language description and thatmatched the search results.
 13. The one or more computer storage mediaof claim 8, wherein the features represented in the word cloud aregenres that were extracted from the natural language description andthat matched the search results.
 14. The one or more computer storagemedia of claim 8, wherein the features represented in the word cloud arerelated artists that were extracted from the natural languagedescription and that matched the search results.
 15. A computer systemcomprising: one or more hardware processors and memory storingcomputer-useable instructions that, when executed by the one or morehardware processors, cause the one or more hardware processors toperform operations comprising: causing a user interface to present aword cloud of features that were extracted from a natural languagedescription and that matched search results of the natural languagedescription; generating, based on an input identifying a selectedfeature of the features from the word cloud, filtered search resultsthat match the selected feature; and causing the user interface topresent a representation of the filtered search results that match theselected feature from the word cloud.
 16. The computer system of claim15, wherein the features are musical features of a music description andcategorization schema, the natural language description is of music, andthe search results are songs.
 17. The computer system of claim 15,wherein the word cloud represents each feature of the features as acorresponding word or phrase having a size that corresponds at least inpart to a count of the search results that matched the feature.
 18. Thecomputer system of claim 15, the operations further comprisingextracting the features by extracting chunks of text from the naturallanguage description and translating the chunks of text intocorresponding features of a description and categorization schema. 19.The computer system of claim 15, wherein the features represented in theword cloud are instances of core schema categories that were extractedfrom the natural language description and that matched the searchresults.
 20. The computer system of claim 15, wherein the featuresrepresented in the word cloud are genres that were extracted from thenatural language description and that matched the search results.